System

Abstract

A system is provided.SOLUTION: A system including means for receiving and storing information such as a user's age, gender, past medical history, current medical history, and request for insurance, means for automatically collecting information on medical insurance services from the Internet using a generative artificial intelligence model, means for analyzing the collected information on the medical insurance services based on the user's information and selecting an optimal insurance product, means for presenting details of the optimal insurance product to the user and generating an answer to an additional question, and means for presenting an appropriate inquiry destination when the generative artificial intelligence model cannot answer.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Based on the scope of the above, the "Problem to be solved by the invention" and "Means for solving the problem" of the patent specification are prepared as follows.

[0005] When selecting medical insurance, users are faced with a vast amount of information, making it difficult to determine which insurance product best suits their needs. Traditional methods often rely on the opinions of insurance salespeople, making it difficult to select the latest products or insurance that is best suited to the individual. Furthermore, gathering information online is time-consuming and has problems with accuracy. This makes it difficult to find the optimal insurance product, which can result in financial and psychological anxiety. To improve this situation, a system is needed that can efficiently and accurately recommend the optimal medical insurance to users. [Means for solving the problem]

[0006] The present invention includes a means for receiving and storing information such as a user's age, gender, medical history, current medical history, and insurance needs, and a means for automatically collecting information on health insurance services from the Internet using a generative artificial intelligence model. The collected information on health insurance services is analyzed based on the user's information to select the most suitable insurance product. The system also includes a means for presenting details of the most suitable insurance product to the user and generating answers to follow-up questions. This allows the user to quickly select the most suitable health insurance service, and also includes a means for presenting appropriate contact information if the generative artificial intelligence model cannot provide an answer. This system significantly simplifies the user's health insurance selection process and improves reliability.

[0007] "User information" refers to information such as the user's age, gender, medical history, current medical history, and insurance needs.

[0008] A "generative artificial intelligence model" is an artificial intelligence that analyzes data based on specific tasks and performs inference and generation in the same way as humans.

[0009] "Means of automatically collecting information from the Internet" refers to a system that uses web scraping or APIs to automatically obtain data from the Internet using a program.

[0010] "Health insurance services" refers to insurance products and plans offered to cover health risks.

[0011] "Insurance product" refers to the insurance service provided by an insurance company based on a contract.

[0012] "Analysis" is the process of using collected data to identify the insurance products that best suit the user's needs.

[0013] "Selection" is the act of determining the most appropriate option based on the analysis results.

[0014] A "user interface" is the part of software that contains the screens and input forms that allow a user to interact with the system.

[0015] A "follow-up question" is a question that allows the user to ask for more specific information about the information displayed.

[0016] "Contact Information" means a location, person, or organization that a user can contact for further information or assistance.

[0017] "Presenting" is the act of displaying information to a user.

[0018] "Automatically collected" refers to the process of smoothly collecting data by program without human intervention.

[0019] "Detailed information" refers to information such as the specific service content, benefits, conditions, and fees of the insurance product. [Brief explanation of the drawings]

[0020] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0021] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0022] First, the terms used in the following description will be explained.

[0023] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0024] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0025] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0026] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0027] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0031] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0032] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0033] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0034] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0035] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0038] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0039] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0040] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0041] The system of the present invention is composed of a combination of software and hardware for implementing a series of processes to propose optimal medical insurance products based on user information. This system provides an interface for inputting the user's age, gender, medical history, current medical history, insurance needs, etc. Based on this, the system uses a generative artificial intelligence model to research and analyze medical insurance services and propose optimal insurance products to the user.

[0042] System Components

[0043] 1. User Interface (Terminal):

[0044] It displays a form for users to enter their information, including age, gender, medical history, current medical history, and insurance preferences.

[0045] 2. Information receiving and storage means (server):

[0046] Receives user information sent from the device and stores it in a database.

[0047] 3. Generative AI model (server):

[0048] A generative artificial intelligence model (e.g., GPT model) is used to collect information on health insurance services from the Internet based on user information.

[0049] Analyze insurance service information and select the insurance product that best suits the user's needs.

[0050] 4. Information provision means (terminal):

[0051] Provide users with detailed information on the most suitable insurance products.

[0052] Generate answers to follow-up questions from the user.

[0053] 5. Contact information providing means (server):

[0054] For questions that the generative artificial intelligence model cannot answer, a list of appropriate contact points is provided to the user.

[0055] Explanation of program processing

[0056] Entering and saving user information

[0057] The user enters their age, gender, medical history, current medical history, and insurance needs. The device receives this information and, when they press the send button, it is sent to the server. The server verifies the received information and saves it in a database.

[0058] Health insurance service survey

[0059] The server uses a generative artificial intelligence model based on the stored user information to collect online health insurance service information, including through web scraping and API usage, and organizes and filters the collected data to best meet the user's needs.

[0060] Insurance product analysis and selection

[0061] The server analyzes the collected information on medical insurance services using a generative AI model and selects the most suitable insurance product based on the user information. This selected information is prepared as data to be presented to the user.

[0062] Presenting results and responding to follow-up questions

[0063] The terminal displays a list of the best insurance products and their details to the user. The user can then enter additional questions based on the list. The server receives the additional questions and generates answers using a generative AI model.

[0064] Dealing with unanswerable questions

[0065] For questions that the generative artificial intelligence model cannot answer, the server will provide the user with a list of appropriate contact points.

[0066] Specific examples

[0067] As a concrete example, let's say a 30-year-old female user has a medical history of asthma, no current medical history, and is seeking generous coverage for hospitalization expenses. The user enters this information into a terminal and sends it. The server receives the information and requests analysis from a generative AI model. The AI ​​collects publicly available medical insurance information and lists the following insurance products:

[0068] Insurance A: Basic medical insurance, covers 80% of hospital costs

[0069] Insurance B: Covers 100% of hospital costs, but is expensive for people with asthma

[0070] Insurance C: A plan that covers 90% of hospitalization costs and is specifically designed for asthma.

[0071] The generative AI model further analyzes this information and proposes the most suitable insurance C to the user. The proposal is presented to the user along with detailed benefits, providing peace of mind to the user.

[0072] The processing flow will be explained below.

[0073] Step 1:

[0074] The user accesses the terminal interface and inputs information such as age, sex, medical history, current medical history, and insurance requirements.

[0075] Step 2:

[0076] The terminal receives information entered by the user, and when the send button is pressed, the information is sent to the server.

[0077] Step 3:

[0078] The server receives the user information sent from the terminal, verifies the information, and then stores it in a database.

[0079] Step 4:

[0080] Based on the stored user information, the server generates a request to the generative artificial intelligence model to investigate medical insurance services.

[0081] Step 5:

[0082] The server collects this information by performing web scraping and API requests to collect information about health insurance services on the Internet.

[0083] Step 6:

[0084] The server analyzes the medical insurance service information collected and compares it with user information to perform filtering.

[0085] Step 7:

[0086] The generative artificial intelligence model (server) uses the analytical data to select the insurance product that best suits the user's needs.

[0087] Step 8:

[0088] Based on the analysis results received by the server from the generative artificial intelligence model, the server generates detailed information and a proposal for the optimal insurance product.

[0089] Step 9:

[0090] The terminal displays the optimum insurance product list and detailed information received from the server to the user.

[0091] Step 10:

[0092] The user checks the displayed insurance information, and if there are any additional questions, enters them in the input form and presses the submit button.

[0093] Step 11:

[0094] The server receives a follow-up question from the user and requests the generative artificial intelligence model to generate an answer.

[0095] Step 12:

[0096] The generative artificial intelligence model (server) generates optimal answers to follow-up questions from the user and sends them back to the server.

[0097] Step 13:

[0098] The server receives the response from the generative artificial intelligence model and converts it into a data format for display to the user.

[0099] Step 14:

[0100] The terminal displays the answer received from the server to the user.

[0101] Step 15:

[0102] For questions that the generative artificial intelligence model cannot answer, the server lists appropriate contact points and presents them to the user.

[0103] Example 1

[0104] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0105] Selecting medical insurance is extremely complex, and it takes time and money for users to find the best insurance product for them. It is also difficult to accurately select an insurance product that meets the needs of each individual user. Furthermore, there is a need for a system that can respond quickly and accurately to additional questions and detailed inquiries. A new system is needed to solve these problems.

[0106] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0107] In this invention, the server includes a means for receiving and storing data on a user's attributes, medical history, and insurance needs; a means for automatically collecting medical insurance service data from a network environment using a generative artificial intelligence model; and a means for analyzing the collected medical insurance service data based on the user's information and selecting an appropriate insurance product. This allows users to easily find the medical insurance product that best suits them. Furthermore, user convenience can be enhanced by providing prompt answers to follow-up questions and appropriate contact information when the generative artificial intelligence model cannot respond.

[0108] "User demographic information" means data regarding a user's age, gender, medical history, current medical history, and specific insurance needs and requirements.

[0109] "Medical history" refers to records of illnesses and treatments a user has experienced in the past.

[0110] A "generative artificial intelligence model" is a program that uses artificial intelligence technology to process natural language, gather information, and analyze data.

[0111] A "network environment" is an information technology infrastructure over which data communication takes place, such as the Internet or an internal network.

[0112] "Health insurance services" are insurance products and plans offered by insurance companies to cover medical expenses and treatment costs.

[0113] "Collection" is the act of gathering data or information for a specific purpose.

[0114] "Analysis" is the process of finding specific patterns and trends based on collected data and drawing evaluations and conclusions.

[0115] "Selection" is the act of choosing the most appropriate option from multiple options.

[0116] "Means for receiving and storing attribute information, medical history, and insurance request data" refers to technology that allows a server to receive and securely store information entered by a user through a terminal.

[0117] "Means for automatically collecting data on health insurance services from a network environment" refers to technology that uses web scraping and APIs to automatically collect necessary data from the Internet and other networks.

[0118] The "means for selecting appropriate insurance products" refers to a technology that enables a generative artificial intelligence model to find the most suitable insurance product based on the user's attribute information and medical history.

[0119] MODE FOR CARRYING OUT THE INVENTION

[0120] The present invention relates to a system that proposes optimal medical insurance products based on data such as user attribute information, medical history, insurance needs, etc. This system has the function of receiving user input information, collecting and analyzing information on medical insurance services using a generative artificial intelligence model, selecting the optimal insurance product, and presenting it to the user.

[0121] System Components

[0122] 1. User Interface (Terminal)

[0123] The terminal provides an interface for users to enter demographic information, medical history, and insurance requirements. This interface displays a form in which users can enter their age, gender, medical history, current medical history, and desired insurance conditions.

[0124] 2. Information receiving and storage means (server)

[0125] The server receives the information sent by the user, checks the content, and stores it in a database, making it possible to reliably manage individual user information.

[0126] 3. Data collection method (server)

[0127] The server uses a generative artificial intelligence model (e.g., GPT model) to automatically collect health insurance data from the network environment, using web scraping technology and APIs.

[0128] 4. Insurance product analysis and selection method (server)

[0129] The server analyzes the collected information on medical insurance services. The generative AI model selects the most suitable insurance product based on the information entered by the user. The analysis results are organized into a list of insurance products that meet the user's needs.

[0130] 5. Information provision means (terminal)

[0131] The terminal displays details of the insurance product provided by the server to the user, and if the user enters any additional questions, the terminal sends them to the server.

[0132] 6. Server for responding to additional questions

[0133] The server receives follow-up questions from the user and uses a generative artificial intelligence model to generate appropriate answers, which are then provided to the user in real time.

[0134] 7. Contact Information Providing Means (Server)

[0135] For questions that the generative artificial intelligence model cannot answer, the server lists appropriate contact information and provides it to the user.

[0136] Specific examples

[0137] For example, let's say a 30-year-old female user has a history of asthma, no current medical history, and wants medical insurance that provides generous coverage for hospitalization expenses. The user enters this information into the device and presses the send button. The server receives the information and uses a generative artificial intelligence model to collect publicly available medical insurance information. The collected data is organized in the following format:

[0138] Insurance A: Basic medical insurance, covers 80% of hospital costs

[0139] Insurance B: Covers 100% of hospital costs, but is expensive for people with asthma

[0140] Insurance C: A plan that covers 90% of hospitalization costs and is specifically designed for asthma.

[0141] The generative AI model analyzes this information and proposes the most suitable insurance C. The proposal, along with details, is presented to the user via their device. The user can then enter more detailed questions, and the server will use the generative AI model to instantly generate an answer. If the server cannot provide an answer, it will provide the appropriate contact information. This process allows users to easily find the medical insurance product that best suits them.

[0142] Prompt Sentence Examples

[0143] "I'm a 30-year-old woman with a medical history of asthma and no current medical conditions. I'm looking for coverage for hospitalization expenses. I'd like you to recommend the most suitable medical insurance product for me."

[0144] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0145] Step 1:

[0146] Users enter their age, gender, medical history, current medical history, and insurance needs into the terminal interface.

[0147] The information entered specifically includes data such as "Age: 30," "Gender: Female," "Medical history: Asthma," and "Insurance preference: Generous coverage for hospitalization expenses."

[0148] The terminal receives this information and temporarily stores it.

[0149] Step 2:

[0150] When the user presses the send button, the terminal sends the entered information to the server.

[0151] The server checks the received data and verifies that there are no omissions or errors.

[0152] The input is user information sent from the terminal.

[0153] The output is the verified user information saved in the database.

[0154] Step 3:

[0155] Based on the stored user information, the server uses a generative artificial intelligence model to collect information on medical insurance services from the network environment.

[0156] The input is user information and a collection prompt (e.g., "30 years old, female. Medical history of asthma, no current medical history. Prioritizing coverage for hospitalization expenses. Please suggest the most suitable medical insurance product.").

[0157] The output is collected health insurance service information data.

[0158] The data is collected using web scraping technology and APIs, specifically by retrieving the latest insurance plan information from insurance companies' official websites and public APIs.

[0159] Step 4:

[0160] The server analyzes the collected medical insurance service information using a generative artificial intelligence model.

[0161] The input is the insurance service data and user information collected in step 3.

[0162] The output is a list of insurance products that best suit the user's needs.

[0163] The generative artificial intelligence model used for the analysis is designed to select the most suitable insurance product based on the user's attribute information.

[0164] Step 5:

[0165] The server transmits the selected optimal insurance product list to the terminal.

[0166] The input is a list of the best insurance products.

[0167] The output is the insurance product details that are presented to the user.

[0168] The terminal displays the received information to the user.

[0169] As a specific example, details of "Insurance C: A plan that covers 90% of hospitalization costs and specializes in asthma" are displayed.

[0170] Step 6:

[0171] The user enters further detailed questions about the displayed insurance product into the terminal.

[0172] The terminal receives the input question and transmits it to the server.

[0173] The input is a follow-up question from the user (e.g., "Does Insurance C cover emergency transport costs?").

[0174] The output is query data to the server.

[0175] Step 7:

[0176] The server passes the received follow-up questions to a generative artificial intelligence model to generate appropriate answers.

[0177] The input is additional question data from the user.

[0178] The output is the generated answer.

[0179] The created answer is sent back to the terminal, which displays it to the user.

[0180] Step 8:

[0181] For questions that the generative artificial intelligence model cannot answer, the server lists appropriate contact points.

[0182] The input is question data that the generative artificial intelligence model cannot answer.

[0183] The output is a list of appropriate contacts.

[0184] This contact information is then sent back to the terminal, which provides it to the user.

[0185] (Application example 1)

[0186] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0187] Conventional medical insurance product proposal systems are limited to providing information on medical insurance products and are unable to fully meet the diverse needs of users. Furthermore, they do not propose optimal payment methods, including payment services, making it difficult for users to find the service that best suits them.

[0188] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0189] In this invention, the server includes means for receiving and storing information such as the user's age, gender, medical history, current medical history, payment history, purchase history, and insurance needs, means for automatically collecting information on medical insurance services and payment services from the Internet using a generative AI model, means for analyzing the collected information on medical insurance services and payment services based on the user's information and selecting the most appropriate insurance product and payment method, means for presenting the user with details of the most appropriate insurance product and payment method and generating answers to any additional questions, and means for presenting an appropriate contact point if the generative AI model cannot provide an answer. This makes it possible to propose optimal medical insurance products and payment methods that meet the diverse needs of users.

[0190] "User Information" refers to information such as the user's age, gender, medical history, current medical history, payment history, purchase history, and insurance requirements.

[0191] A "generative artificial intelligence model" refers to an artificial intelligence model that collects information from the Internet and makes optimal suggestions based on user information.

[0192] "Medical insurance services" refers to services that provide optimal medical insurance products based on the user's health condition and insurance needs.

[0193] "Payment services" refers to a means of providing optimal payment methods and discount services based on a user's payment history and purchase history.

[0194] "User interface" refers to the screens and forms through which users enter information and view system results.

[0195] "Web scraping" refers to the technique of automatically collecting information on the Internet.

[0196] "Contact Information" refers to the appropriate contact point for users to inquire about questions that the generative AI model cannot answer.

[0197] MODE FOR CARRYING OUT THE INVENTION

[0198] System Components

[0199] 1. User interface (smartphone app)

[0200] The user interface provides a form for users to enter their own information, including age, gender, medical history, current medical history, payment history, purchase history, and insurance needs. For example, by launching a smartphone app and entering information into the input form displayed on the screen, this data is sent to the system.

[0201] 2. Information receiving and storage means (cloud server)

[0202] The cloud server receives user information sent from the smartphone app and stores it in a database, where it is safely stored and can be accessed whenever necessary.

[0203] 3. Generative AI model (cloud server)

[0204] The cloud server uses a generative artificial intelligence model (e.g., GPT model) based on the stored user information to collect information on health insurance services and payment services from the internet. This includes using web scraping technology and APIs to quickly obtain the latest information. The collected data is organized on the cloud server and filtered in an optimal manner based on the user's needs.

[0205] 4. Insurance product analysis and selection

[0206] The cloud server analyzes the collected information on medical insurance services and payment services using a generative AI model. Based on the user information, it selects the most suitable insurance product and payment method, and prepares this selected information as data to present to the user.

[0207] 5. Information provision method (smartphone app)

[0208] The smartphone app displays detailed information on the optimal insurance product and payment method selected for the user. The user can then enter additional questions based on this information. The cloud server receives the additional questions and generates answers using a generative artificial intelligence model.

[0209] 6. Dealing with unanswerable questions

[0210] For questions that the generative AI model cannot answer, the cloud server will provide a list of appropriate contact points to the user, allowing them to quickly obtain the information they need.

[0211] Hardware and software used

[0212] The system is implemented using the following hardware and software:

[0213] Smartphone app (e.g. Android or iOS)

[0214] Cloud servers (e.g. AWS, Google Cloud, Microsoft Azure)

[0215] Databases (e.g. AWS RDS, Google Cloud SQL, Microsoft Azure SQL Database)

[0216] Generative AI models (e.g., OpenAI GPT-3, other AI models)

[0217] Web scraping technology (e.g., Beautiful Soup, Scrapy)

[0218] Specific examples

[0219] As a concrete example, consider a 30-year-old female user who has a medical history of asthma, no current medical history, spends a lot of money on groceries, and seeks generous coverage for hospital expenses. The user enters this information into an input form on a smartphone app and submits it. The cloud server receives the information and requests analysis from a generative artificial intelligence model. The AI ​​collects publicly available information on medical insurance and payment services, and lists the following insurance products and payment methods:

[0220] Insurance A: Basic medical insurance, covers 80% of hospital costs

[0221] Insurance B: Covers 100% of hospital costs, but is expensive for people with asthma

[0222] Insurance C: A plan that covers 90% of hospitalization costs and is specifically designed for asthma.

[0223] Payment Service A: Offers 5% cash back on grocery purchases

[0224] Payment Service B: Receive a 20% discount on grocery purchases over a certain amount each month

[0225] The generative AI model analyzes this information and recommends the most suitable insurance C and payment service A for the user. The recommendations, along with detailed benefits, are displayed on the smartphone app, providing peace of mind and savings opportunities for the user.

[0226] Prompt Sentence Examples

[0227] User Information:

[0228] Age: 30

[0229] Gender: Female

[0230] Payment history: Breakdown of monthly expenses (groceries 60%, eating out 20%, other 20%)

[0231] Insurance demand: cash back and discounts on groceries

[0232] Please suggest the best insurance product and payment service for this user."

[0233] The above is a specific embodiment for carrying out the invention, which allows users to receive proposals for optimal insurance products and payment methods that meet their diverse needs.

[0234] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0235] Step 1:

[0236] Input: The user enters information such as age, gender, medical history, current medical history, payment history, purchase history, and insurance requirements.

[0237] Action: A user uses a smartphone app to enter the required information into a form and presses the submit button.

[0238] Output: The entered user information is sent from the app to the server.

[0239] Step 2:

[0240] Input: Data containing user information is sent to the server.

[0241] How it works: The server analyzes the received user information and stores the data in a database.

[0242] Output: User information is saved in the database of the cloud server.

[0243] Step 3:

[0244] Input: A dataset is created based on the stored user information to be input into the generative artificial intelligence model.

[0245] How it works: The server uses a generative artificial intelligence model to collect information about health insurance and payment services from the internet. It obtains information using web scraping technology and APIs.

[0246] Output: The collected information on medical insurance services and payment services is sent to the server and organized.

[0247] Step 4:

[0248] Input: Collected and organized information on health insurance services and payment services is passed to the generative AI model.

[0249] How it works: The server analyzes the collected information using a generative artificial intelligence model and selects the most appropriate insurance product and payment method based on the user information.

[0250] Output: A list of optimal insurance products and payment methods is generated as a result of the analysis.

[0251] Step 5:

[0252] Input: A list of insurance products and payment methods is generated as a result of the analysis.

[0253] How it works: The server sends the generated list to the smartphone app.

[0254] Output: Detailed information on the best insurance products and payment methods for your smartphone app will be displayed.

[0255] Step 6:

[0256] Input: The user enters a follow-up question.

[0257] How it works: A user enters and submits a follow-up question through a smartphone app. The server uses a generative artificial intelligence model to generate an answer for the received question.

[0258] Output: The generated answers are displayed on the smartphone app.

[0259] Step 7:

[0260] Input: A question that the generative artificial intelligence model cannot answer is input.

[0261] How it works: The server lists appropriate contact points for users when they have questions that the generative AI model cannot answer, and provides them to them.

[0262] Output: A list of contacts is displayed on the smartphone app.

[0263] The above are the specific processing steps of this system's program. Through this series of processes, users can find the most suitable medical insurance product and payment method for them and obtain detailed information.

[0264] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0265] The system of the present invention is composed of a combination of software and hardware for implementing a series of processes to propose optimal medical insurance products based on user information. This system also includes an emotion engine that recognizes the user's emotions, enabling more personalized proposals and responses depending on the user's emotional state.

[0266] System Components

[0267] 1. User Interface (Terminal):

[0268] It displays a form for users to enter their information, including age, gender, medical history, current medical history, and insurance preferences.

[0269] Emotion data is acquired using a camera or microphone while the user is typing.

[0270] 2. Information receiving and storage means (server):

[0271] Receives user information and emotion data sent from the device and stores it in a database.

[0272] 3. Generative AI model (server):

[0273] A generative artificial intelligence model (e.g., GPT model) is used to collect information on health insurance services from the Internet based on user information.

[0274] Analyze insurance service information and select the insurance product that best suits the user's needs.

[0275] 4. Emotion engine (server):

[0276] The system recognizes emotions from user input and interactions, and reflects the results in insurance product suggestions and responses.

[0277] 5. Information provision means (terminal):

[0278] Provide users with detailed information on the most suitable insurance products.

[0279] Generate answers to follow-up questions from the user.

[0280] Present information and respond in different formats based on the user's emotional state.

[0281] 6. Contact information providing means (server):

[0282] For questions that the generative artificial intelligence model cannot answer, a list of appropriate contact points is provided to the user.

[0283] Explanation of program processing

[0284] Entering and saving user information

[0285] The user inputs their age, gender, medical history, current medical history, and insurance needs. The device receives this information and, upon pressing the send button, sends it to the server. During this process, the device's camera and microphone are used to capture the user's emotional data. The server checks the received information and emotional data and stores it in a database.

[0286] Health insurance service survey

[0287] The server uses a generative artificial intelligence model based on the stored user information to collect online health insurance service information, including through web scraping and API usage, and organizes and filters the collected data to best meet the user's needs.

[0288] Insurance product analysis and selection

[0289] The server analyzes the collected information on medical insurance services using a generative AI model and selects the most suitable insurance product based on the user's information and emotional data. This selected information is prepared as data to be presented to the user.

[0290] Utilizing Emotional Data

[0291] The emotion engine (server) analyzes the stored emotional data and determines whether the user is relaxed, stressed, etc. Based on this information, the generative AI model presents detailed information about insurance products to the user.

[0292] Presenting results and responding to follow-up questions

[0293] The terminal displays a list of selected optimal insurance products and their detailed information to the user. The user can then enter additional questions based on this information. The server receives the additional questions and requests a generative AI model to generate answers. Information is provided in an appropriate format based on the user's emotional state.

[0294] Dealing with unanswerable questions

[0295] For questions that the generative artificial intelligence model cannot answer, the server will provide the user with a list of appropriate contact points.

[0296] Specific examples

[0297] As a concrete example, let's say a 30-year-old female user has a medical history of asthma, no current medical history, and is seeking generous coverage for hospitalization expenses. The user enters this information into a terminal and sends it. The server receives the information and requests analysis from a generative AI model. The AI ​​collects publicly available medical insurance information and lists the following insurance products:

[0298] Insurance A: Basic medical insurance, covers 80% of hospital costs

[0299] Insurance B: Covers 100% of hospital costs, but is expensive for people with asthma

[0300] Insurance C: A plan that covers 90% of hospitalization costs and is specifically designed for asthma.

[0301] The generative AI model further analyzes this information and proposes the most suitable insurance C to the user. The emotion engine analyzes the user's emotions and presents the information in an appropriate format based on stress and sense of security. The proposal is presented to the user along with detailed benefits, providing peace of mind to the user.

[0302] The processing flow will be explained below.

[0303] Step 1:

[0304] The user accesses the terminal interface and inputs information such as age, sex, medical history, current medical history, and insurance requirements.

[0305] Step 2:

[0306] The device receives information input by the user and simultaneously acquires the user's emotional data using the device's camera and microphone.

[0307] Step 3:

[0308] When the terminal presses the send button, the user information and emotion data are sent to the server.

[0309] Step 4:

[0310] The server receives the information and emotion data sent by the user and stores them in a database.

[0311] Step 5:

[0312] Based on the stored user information, the server generates a request to the generative artificial intelligence model to investigate medical insurance services.

[0313] Step 6:

[0314] The server collects this information by performing web scraping and API requests to collect information about health insurance services on the Internet.

[0315] Step 7:

[0316] The server analyzes the medical insurance service information collected and compares it with user information to perform filtering.

[0317] Step 8:

[0318] The generative artificial intelligence model (server) uses the analytical data to select the insurance product that best suits the user's needs.

[0319] Step 9:

[0320] Based on the analysis results received by the server from the generative artificial intelligence model, the server generates detailed information and a proposal for the optimal insurance product.

[0321] Step 10:

[0322] The server uses an emotion engine to analyze the stored emotion data and determine the user's current emotional state.

[0323] Step 11:

[0324] Based on the analysis results from the emotion engine, the server optimizes detailed information and proposals for insurance products in a format that corresponds to the user's emotional state.

[0325] Step 12:

[0326] The terminal displays the optimized insurance product list and detailed information received from the server to the user.

[0327] Step 13:

[0328] The user checks the displayed insurance information, and if there are any additional questions, enters them in the input form and presses the submit button.

[0329] Step 14:

[0330] The server receives a follow-up question from the user and requests the generative artificial intelligence model to generate an answer.

[0331] Step 15:

[0332] The generative artificial intelligence model (server) generates optimal answers to follow-up questions from the user and sends them back to the server.

[0333] Step 16:

[0334] The server receives the response from the generative artificial intelligence model and converts it into a data format for display to the user.

[0335] Step 17:

[0336] The terminal displays the answer received from the server to the user.

[0337] Step 18:

[0338] For questions that the generative artificial intelligence model cannot answer, the server lists appropriate contact points and presents them to the user.

[0339] Example 2

[0340] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0341] Conventional medical insurance product recommendation systems have difficulty proposing optimal insurance products that meet the individual needs of users, and have a particular problem of lacking personalized recommendations based on emotional states. Another problem is that when the generative AI model cannot answer a question, it takes time and effort for the user to find the appropriate contact point.

[0342] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0343] In this invention, the server includes means for receiving and storing personal information such as the user's age, gender, medical history, current medical history, and insurance needs, means for automatically collecting information on medical insurance services from the Internet using a generative AI model, means for analyzing the collected information on medical insurance services based on the user's personal information and selecting the most suitable insurance product, means for acquiring the user's emotional data and customizing the insurance product proposal content and information provision format based on the data, means for presenting the user with details of the most suitable insurance product and generating answers to additional questions, and means for presenting an appropriate contact point if the generative AI model cannot provide an answer. This makes it possible to propose personalized insurance products based on emotions and quickly provide contact points for inquiries to the user.

[0344] "User's personal information" refers to information that is useful for identifying a user and proposing insurance products, such as the user's age, gender, medical history, current medical history, and insurance needs.

[0345] A "generative AI model" refers to a model that uses artificial intelligence technology to collect information from the internet and generate data based on that information.

[0346] "Means of automatically collecting information on health insurance services from the Internet" refers to the process of automatically collecting information on health insurance services that are publicly available on the Internet using technologies such as web scraping and the use of APIs.

[0347] "Means for acquiring emotional data" refers to technology that uses hardware such as cameras and microphones to collect the user's facial expressions and voice, and then analyzes the user's emotional state based on this.

[0348] "Means for selecting the most suitable insurance product" refers to technology that analyzes the user's personal information and collected information on medical insurance services to select the insurance product that is most suitable for the user.

[0349] "Means for customizing the content of suggestions and the format of information provided" refers to the process of adjusting and customizing the content and format of information provided to users based on the acquired emotional data.

[0350] "Means for providing appropriate contact information" refers to technology for providing relevant contact information to the user when the generative AI model is unable to answer the user's follow-up question.

[0351] The system of the present invention is comprised of a combination of software and hardware for implementing a series of processes to recommend optimal health insurance products based on user information. The system includes an emotion engine that recognizes the user's emotional state and makes personalized recommendations accordingly.

[0352] System Components

[0353] 1. User Interface (Terminal):

[0354] It displays a form for users to enter their information, including age, gender, medical history, current medical history, and insurance preferences.

[0355] Emotion data is acquired using a camera or microphone while the user is typing.

[0356] 2. Information receiving and storage means (server):

[0357] Receives user information and emotion data sent from the device and stores it in a database.

[0358] 3. Generative AI model (server):

[0359] A generative artificial intelligence model (e.g., GPT model) is used to collect information on health insurance services from the Internet based on user information.

[0360] Analyze insurance service information and select the insurance product that best suits the user's needs.

[0361] 4. Emotion engine (server):

[0362] The system recognizes emotions from user input and interactions, and reflects the results in insurance product suggestions and responses.

[0363] 5. Information provision means (terminal):

[0364] Provide users with detailed information on the most suitable insurance products.

[0365] Generate answers to follow-up questions from the user.

[0366] Present information and respond in different formats based on the user's emotional state.

[0367] 6. Contact information providing means (server):

[0368] For questions that the generative AI model cannot answer, a list of appropriate contact points is provided to the user.

[0369] Explanation of program processing

[0370] Entering and saving user information

[0371] The user inputs their age, gender, medical history, current medical history, and insurance needs on the device. The device receives this information, and when the user presses the send button, emotional data obtained using the camera and microphone is sent to the server along with the information.

[0372] Collection of medical insurance service information

[0373] The server uses stored user information to gather health insurance service information from the internet using a generative artificial intelligence model, including web scraping and API usage.

[0374] Insurance product analysis and selection

[0375] The server uses a generative AI model to analyze the collected information on medical insurance services. Based on user information and emotional data, it selects the most suitable insurance product and organizes the proposal.

[0376] Utilizing Emotional Data

[0377] The emotion engine (server) analyzes the stored emotion data and determines whether the user is relaxed or stressed. Based on this result, it customizes the format and content of the information provided.

[0378] Presenting optimal insurance products and responding to additional questions

[0379] The terminal displays a list of the best insurance products and their details to the user. The information provided to the user is presented in an appropriate format based on their emotional state. The user can then enter additional questions based on the information presented.

[0380] Responding to any additional questions and providing contact information

[0381] The server receives the user's follow-up questions and asks the generative AI model to generate an answer. If the generative AI model cannot answer the question, the server lists appropriate contact points and provides them to the user.

[0382] Specific examples

[0383] As a concrete example, let's say a 30-year-old female user has a medical history of asthma, no current medical history, and is seeking generous coverage for hospitalization expenses. The user enters this information into a terminal and sends it. The server receives the information and requests analysis from a generative AI model. The generative AI collects publicly available medical insurance information and lists the following insurance products:

[0384] Insurance A: Basic medical insurance, covers 80% of hospital costs

[0385] Insurance B: Covers 100% of hospital costs, but is expensive for people with asthma

[0386] Insurance C: A plan that covers 90% of hospitalization costs and is specifically designed for asthma.

[0387] The server further analyzes this information and proposes the most suitable insurance C to the user. The emotion engine analyzes the user's emotions and presents the information in an appropriate format based on stress and sense of security. The proposal is presented to the user along with detailed benefits, providing peace of mind to the user.

[0388] Example of input prompt for the generative AI model to be used

[0389] "I'm a 30-year-old woman with a history of asthma and no current medical conditions. I'm looking for medical insurance that provides comprehensive coverage for hospitalization costs. Which insurance would be best for me?"

[0390] This system automatically suggests the most suitable medical insurance product based on the user's input information and emotional data, and provides personalized responses based on emotions, making it easier for users to find the insurance product that is most suitable for them.

[0391] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0392] Program processing steps

[0393] Step 1: Enter user information and obtain emotion data

[0394] The user enters their age, gender, medical history, current medical history, and insurance needs into the input form on the terminal. The input information is received by the terminal.

[0395] Input: Personal information entered by the user (e.g., age, gender, medical history, current medical history, insurance requests)

[0396] Output: Received user information and emotion data

[0397] Specific operation: The user enters information into the form, such as "30 years old, female, with asthma, looking for insurance that covers hospitalization costs generously." The device then activates the camera to capture the user's facial expressions and uses the microphone to obtain voice data.

[0398] Step 2: Send and store user information and emotion data

[0399] The device sends the acquired user information and emotion data to the server, which then stores the received information in a database.

[0400] Input: Received user information and emotion data

[0401] Output: User information and emotion data stored in a database

[0402] Specific operation: The device sends information such as "Age: 30, Gender: Female, Medical history: Asthma, Current medical history: None, Request: Coverage of hospital expenses, Emotional data: Stress level 3, Peace of mind level 2" to the server, which then stores it in a database.

[0403] Step 3: Collect health insurance service information

[0404] The server uses a generative artificial intelligence model based on the stored user information to gather information about health insurance services from the internet, including through web scraping and API usage.

[0405] Input: User information stored in the database

[0406] Output: Collected health insurance service information

[0407] Specific operation: The server inputs the following prompt sentence into the generative AI model: "30 years old, female, with a history of asthma, looking for medical insurance that prioritizes hospitalization costs. Please give me a list of recommended insurance products." The generative AI model collects medical insurance information from the web and returns a list of multiple insurance products such as "Insurance A, Insurance B, Insurance C."

[0408] Step 4: Insurance product analysis and selection

[0409] The server analyzes the collected information on medical insurance services using a generative AI model, and selects the most suitable insurance product based on user information and emotional data.

[0410] Input: Collected medical insurance service information, user information, and emotion data

[0411] Output: Recommendations for optimal insurance products

[0412] Specific operation: The server selects Insurance C, which best suits the user's needs, from the collected information: Insurance A (80% coverage), Insurance B (100% coverage, high asthma premium), and Insurance C (90% coverage). The selected information is organized into a detailed proposal.

[0413] Step 5: Analyze emotional data and customize information

[0414] The emotion engine (server) analyzes the stored emotion data to determine the user's emotional state, and customizes the information presentation format based on this result.

[0415] Input: Emotion data

[0416] Output: customized information format

[0417] Specific operation: The emotion engine (server) determines from the emotion data that the user's stress level is high and generates a detailed and polite explanation.

[0418] Step 6: Present the best insurance product and receive additional questions

[0419] The terminal displays a list of the best insurance products and their details to the user, who can then enter additional questions based on the information displayed.

[0420] Input: Recommendation for the best insurance product

[0421] Output: Insurance product details presented to the user

[0422] What happens: The device displays detailed information such as "Insurance C: A plan that covers 90% of hospitalization costs. Specializes in asthma, monthly premium is 8000 yen." The user then enters an additional question: "What benefits does Insurance C offer?"

[0423] Step 7: Generate answers to follow-up questions

[0424] The server receives the user's follow-up question, asks the generative AI model to generate an answer, and presents the generated answer to the user.

[0425] Input: User's additional question

[0426] Output: The generated answer

[0427] Specific operation: The server inputs "Additional question from the user: What benefits does Insurance C offer?" into the generative AI model, obtains an answer from the generative AI model such as "Insurance C offers benefits such as transportation subsidies for hospitalization and online consultations with a dedicated doctor," and presents this to the user.

[0428] Step 8: Provide contact information for unanswered questions

[0429] For questions that the generative AI model cannot answer, the server lists appropriate contact points and provides them to the user.

[0430] Input: Notification that the generative AI model is unable to answer the question

[0431] Output: Appropriate contact information

[0432] Specific operation: The server investigates questions that the generative AI model cannot answer and provides the user with information such as "For more information, please contact the following: Insurance Company C, Inquiry Number: XXX-XXXX-XXXX."

[0433] (Application example 2)

[0434] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0435] Conventional medical insurance product recommendation systems make recommendations without taking the user's emotional state into consideration, which can make it difficult for users to understand and accept them with a sense of security. While conventional systems can collect information and make recommendations using generative artificial intelligence models, they lack personalization using emotional data. When a user is stressed or not relaxed, it is difficult to provide appropriate information, and there is a need to improve user satisfaction.

[0436] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0437] In this invention, the server includes means for receiving and storing information such as the user's age, gender, medical history, current medical history, and insurance needs, means including an emotion engine for recognizing the user's emotions, means for automatically collecting information on medical insurance services from the Internet using a generative artificial intelligence model, means for analyzing the collected information on medical insurance services based on the user's information and emotional data and selecting an optimal insurance product, means for presenting details of the optimal insurance product in a format appropriate to the user's emotional state and generating answers to follow-up questions, and means for presenting an appropriate contact point if the generative artificial intelligence model cannot provide an answer. This makes it possible to propose individual insurance products according to the user's emotional state and to propose information that the user can accept with peace of mind.

[0438] "User information" refers to personal information entered by the user, such as age, gender, medical history, current medical history, and insurance requests.

[0439] An "emotion engine" is a system or software for recognizing user emotions and analyzing that data.

[0440] A "generative artificial intelligence model" is a system that includes machine learning algorithms to generate appropriate information and make suggestions based on user input.

[0441] A "user interface" is a system or screen configuration that provides a screen or form for a user to enter information.

[0442] "Emotional data" refers to emotional data based on facial expressions, tone of voice, body movements, etc., captured using the user's camera or microphone.

[0443] "Web scraping" is a technical method for automatically collecting data from the Internet.

[0444] "Insurance service information" refers to information about the most suitable medical insurance products that meet the user's needs.

[0445] "Contact Information" refers to the appropriate contact point for the user to obtain additional information or assistance if the generative AI model is unable to provide an answer.

[0446] "Pre-selection" is the process of selecting the most suitable medical insurance product based on user information.

[0447] System Configuration

[0448] The system according to the present invention comprises the following elements:

[0449] 1. User Interface (Terminal):

[0450] It displays a form for the user to enter their information, including age, gender, medical history, current medical history, and insurance preferences.

[0451] Emotion data is acquired using a camera or microphone while the user is typing.

[0452] 2. Information receiving and storage means (server):

[0453] Receives user information and emotion data sent from the device and stores it in a database.

[0454] 3. Generative AI model (server):

[0455] A generative artificial intelligence model is used to collect information on medical insurance services from the Internet based on user information.

[0456] Analyze insurance service information and select the insurance product that best suits the user's needs.

[0457] 4. Emotion engine (server):

[0458] The system recognizes emotions from user input and interactions, and reflects the results in insurance product suggestions and responses.

[0459] 5. Information provision means (terminal):

[0460] Provide users with detailed information on the most suitable insurance products.

[0461] Generate answers to follow-up questions from the user.

[0462] Present information and respond in different formats based on the user's emotional state.

[0463] 6. Contact information providing means (server):

[0464] For questions that the generative artificial intelligence model cannot answer, a list of appropriate contact points is provided to the user.

[0465] Program processing

[0466] The server receives the user's age, gender, medical history, current medical history, and insurance needs through the device when the user enters them, as well as emotional data captured using the camera and microphone when the user enters the information, and stores this in a database.

[0467] The generative AI model automatically collects information about health insurance services from the internet based on the received user information. This information is collected using web scraping technology. The collected data is analyzed, and the optimal insurance product is selected taking into account the user's information and emotional data.

[0468] The emotion engine analyzes the stored emotional data to determine whether the user is relaxed, stressed, etc. Based on this emotional information, the generative AI model selects detailed information about insurance products and presents them to the user in an appropriate format.

[0469] The terminal displays a list of selected optimal insurance products and their detailed information to the user. The user can then enter additional questions based on this information. The server receives the additional questions and requests the generative AI model to generate an answer. Information is provided in an appropriate format based on the user's emotional state. For questions that the generative AI model cannot answer, the server lists appropriate contact points and provides them to the user.

[0470] Specific examples

[0471] As a concrete example, consider a 30-year-old female user with a medical history of asthma, no current medical history, and a desire for generous coverage for hospitalization expenses. The user enters this information into a terminal and sends it. The server receives the information and requests analysis from a generative AI model. The AI ​​collects publicly available medical insurance information and lists the following insurance products, for example:

[0472] Insurance A: Basic medical insurance, covers 80% of hospital costs

[0473] Insurance B: Covers 100% of hospital costs, but is expensive for people with asthma

[0474] Insurance C: A plan that covers 90% of hospitalization costs and is specifically designed for asthma.

[0475] The generative AI model further analyzes this information and proposes the most suitable insurance C to the user. The emotion engine analyzes the user's emotions, and the information is presented in an appropriate format based on stress and sense of security.

[0476] Example prompts to input to the generative AI model

[0477] User info:

[0478] Age: 30

[0479] Gender: Female

[0480] Medical history: Asthma

[0481] Current medical status: None

[0482] Insurance needs: Comprehensive hospitalization coverage

[0483] User emotion analysis:

[0484] Current emotion: Nervous

[0485] Based on the above information and current emotion, what is the best health insurance plan for this user? Provide detailed plan options.

[0486] This prompt is then fed into a generative AI model, which then suggests appropriate insurance products in real time and can flexibly respond to changes in the user's emotions.

[0487] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0488] Specific explanations divided into processing steps

[0489] Step 1:

[0490] Entering and saving user information

[0491] The user enters their age, gender, medical history, current medical history, and insurance needs. The device receives this information and, upon pressing the send button, sends it to the server. During this process, the device's camera and microphone are used to acquire the user's emotional data. The input is the user's personal information and emotional data, and the output is sent to the server. Data processing involves converting and saving the user information and emotional data in a format with metadata.

[0492] Step 2:

[0493] Store user information and emotional data

[0494] The server receives user information and emotion data sent from the device. The received information is stored in a database. User information and emotion data are sent to the server as input, and this information is stored in a database as output. Data processing involves normalizing and verifying the received information.

[0495] Step 3:

[0496] Gathering information on medical insurance services

[0497] The server uses a generative artificial intelligence model based on the stored user information to collect online health insurance service information. Web scraping technology is used for collection. User information and website URLs are input, and health insurance service data is obtained as output. Data processing involves structuring the collected data and filtering out information relevant to the user.

[0498] Step 4:

[0499] Analysis and selection of medical insurance service information

[0500] The server analyzes the collected medical insurance service information using a generative AI model and selects the most suitable insurance product based on user information and emotional data. The input is medical insurance service information and user information, and the output is a list of the most suitable insurance products. In data processing, the emotional data is linked to the user's needs, and the generative AI model selects the appropriate insurance product.

[0501] Step 5:

[0502] Emotion data analysis and results presentation

[0503] The emotion engine analyzes the stored emotion data and determines whether the user is relaxed or stressed. Based on this information, the server presents the user with detailed information on the most suitable insurance products. The input is emotion data and a list of the most suitable insurance products, and the output is a customized presentation format and information content. Specifically, if the user is feeling stressed, softer language and explanation methods are used.

[0504] Step 6:

[0505] Generate answers to additional questions

[0506] The user can enter additional questions based on the insurance product presented. The device receives these and sends them to the server. The server then requests the generative AI model to generate an answer. The input is the additional question and related user information, and the output is the generated answer, which is displayed on the device. In terms of data calculation, the generative AI model generates an appropriate answer from a number of information sources.

[0507] Step 7:

[0508] Providing contact information

[0509] For questions that the generative AI model cannot answer, the server lists appropriate contact points and provides them to the user. The input is the unanswerable question and user information, and the output is the contact information displayed to the user. Data processing involves searching the knowledge base of the generative AI model for the most appropriate contact point.

[0510] Specific example processing explanation

[0511] For example, when a 30-year-old female user seeks insurance advice, she enters information into the device and uses a camera to collect facial expression data. The server receives and stores this data, then uses a generative AI model to select the most suitable insurance product. An emotion engine then analyzes the user's emotions and presents information based on the stress the user is feeling. Finally, the generative AI model suggests insurance products and responds to any follow-up questions.

[0512] Example prompts to input to a generative AI model:

[0513] User info:

[0514] Age: 30

[0515] Gender: Female

[0516] Medical history: Asthma

[0517] Current medical status: None

[0518] Insurance needs: Comprehensive hospitalization coverage

[0519] User emotion analysis:

[0520] Current emotion: Nervous

[0521] Based on the above information and current emotion, what is the best health insurance plan for this user? Provide detailed plan options.

[0522] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0523] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0524] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0525] [Second embodiment]

[0526] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0527] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0528] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0529] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0530] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0531] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0532] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0533] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0534] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0535] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0536] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0537] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0538] The system of the present invention is composed of a combination of software and hardware for implementing a series of processes to propose optimal medical insurance products based on user information. This system provides an interface for inputting the user's age, gender, medical history, current medical history, insurance needs, etc., and then uses a generative artificial intelligence model to research and analyze medical insurance services and propose optimal insurance products to the user.

[0539] System Components

[0540] 1. User Interface (Terminal):

[0541] It displays a form for users to enter their information, including age, gender, medical history, current medical history, and insurance preferences.

[0542] 2. Information receiving and storage means (server):

[0543] Receives user information sent from the device and stores it in a database.

[0544] 3. Generative AI model (server):

[0545] A generative artificial intelligence model (e.g., GPT model) is used to collect information on health insurance services from the Internet based on user information.

[0546] Analyze insurance service information and select the insurance product that best suits the user's needs.

[0547] 4. Information provision means (terminal):

[0548] Provide users with detailed information on the most suitable insurance products.

[0549] Generate answers to follow-up questions from the user.

[0550] 5. Contact information providing means (server):

[0551] For questions that the generative artificial intelligence model cannot answer, a list of appropriate contact points is provided to the user.

[0552] Explanation of program processing

[0553] Entering and saving user information

[0554] The user enters their age, gender, medical history, current medical history, and insurance needs. The device receives this information and, when they press the send button, it is sent to the server. The server verifies the received information and saves it in a database.

[0555] Health insurance service survey

[0556] The server uses a generative artificial intelligence model based on the stored user information to collect online health insurance service information, including through web scraping and API usage, and organizes and filters the collected data to best meet the user's needs.

[0557] Insurance product analysis and selection

[0558] The server analyzes the collected information on medical insurance services using a generative AI model and selects the most suitable insurance product based on the user information. This selected information is prepared as data to be presented to the user.

[0559] Presenting results and responding to follow-up questions

[0560] The terminal displays a list of the best insurance products and their details to the user. The user can then enter additional questions based on the list. The server receives the additional questions and generates answers using a generative AI model.

[0561] Dealing with unanswerable questions

[0562] For questions that the generative artificial intelligence model cannot answer, the server will provide the user with a list of appropriate contact points.

[0563] Specific examples

[0564] As a concrete example, let's say a 30-year-old female user has a medical history of asthma, no current medical history, and is seeking generous coverage for hospitalization expenses. The user enters this information into a terminal and sends it. The server receives the information and requests analysis from a generative AI model. The AI ​​collects publicly available medical insurance information and lists the following insurance products:

[0565] Insurance A: Basic medical insurance, covers 80% of hospital costs

[0566] Insurance B: Covers 100% of hospital costs, but is expensive for people with asthma

[0567] Insurance C: A plan that covers 90% of hospitalization costs and is specifically designed for asthma.

[0568] The generative AI model further analyzes this information and proposes the most suitable insurance C to the user. The proposal is presented to the user along with detailed benefits, providing peace of mind to the user.

[0569] The processing flow will be explained below.

[0570] Step 1:

[0571] The user accesses the terminal interface and inputs information such as age, sex, medical history, current medical history, and insurance requirements.

[0572] Step 2:

[0573] The terminal receives information entered by the user, and when the send button is pressed, the information is sent to the server.

[0574] Step 3:

[0575] The server receives the user information sent from the terminal, verifies the information, and then stores it in a database.

[0576] Step 4:

[0577] Based on the stored user information, the server generates a request to the generative artificial intelligence model to investigate medical insurance services.

[0578] Step 5:

[0579] The server collects this information by performing web scraping and API requests to collect information about health insurance services on the Internet.

[0580] Step 6:

[0581] The server analyzes the medical insurance service information collected and compares it with user information to perform filtering.

[0582] Step 7:

[0583] The generative artificial intelligence model (server) uses the analytical data to select the insurance product that best suits the user's needs.

[0584] Step 8:

[0585] Based on the analysis results received by the server from the generative artificial intelligence model, the server generates detailed information and a proposal for the optimal insurance product.

[0586] Step 9:

[0587] The terminal displays the optimum insurance product list and detailed information received from the server to the user.

[0588] Step 10:

[0589] The user checks the displayed insurance information, and if there are any additional questions, enters them in the input form and presses the submit button.

[0590] Step 11:

[0591] The server receives a follow-up question from the user and requests the generative artificial intelligence model to generate an answer.

[0592] Step 12:

[0593] The generative artificial intelligence model (server) generates optimal answers to follow-up questions from the user and sends them back to the server.

[0594] Step 13:

[0595] The server receives the response from the generative artificial intelligence model and converts it into a data format for display to the user.

[0596] Step 14:

[0597] The terminal displays the answer received from the server to the user.

[0598] Step 15:

[0599] For questions that the generative artificial intelligence model cannot answer, the server lists appropriate contact points and presents them to the user.

[0600] Example 1

[0601] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0602] Selecting medical insurance is extremely complex, and it takes time and money for users to find the best insurance product for them. It is also difficult to accurately select an insurance product that meets the needs of each individual user. Furthermore, there is a need for a system that can respond quickly and accurately to additional questions and detailed inquiries. A new system is needed to solve these problems.

[0603] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0604] In this invention, the server includes a means for receiving and storing data on a user's attributes, medical history, and insurance needs; a means for automatically collecting medical insurance service data from a network environment using a generative artificial intelligence model; and a means for analyzing the collected medical insurance service data based on the user's information and selecting an appropriate insurance product. This allows users to easily find the medical insurance product that best suits them. Furthermore, user convenience can be enhanced by providing prompt answers to follow-up questions and appropriate contact information when the generative artificial intelligence model cannot respond.

[0605] "User demographic information" means data regarding a user's age, gender, medical history, current medical history, and specific insurance needs and requirements.

[0606] "Medical history" refers to records of illnesses and treatments a user has experienced in the past.

[0607] A "generative artificial intelligence model" is a program that uses artificial intelligence technology to process natural language, gather information, and analyze data.

[0608] A "network environment" is an information technology infrastructure over which data communication takes place, such as the Internet or an internal network.

[0609] "Health insurance services" are insurance products and plans offered by insurance companies to cover medical expenses and treatment costs.

[0610] "Collection" is the act of gathering data or information for a specific purpose.

[0611] "Analysis" is the process of finding specific patterns and trends based on collected data and drawing evaluations and conclusions.

[0612] "Selection" is the act of choosing the most appropriate option from multiple options.

[0613] "Means for receiving and storing attribute information, medical history, and insurance request data" refers to technology that allows a server to receive and securely store information entered by a user through a terminal.

[0614] "Means for automatically collecting data on health insurance services from a network environment" refers to technology that uses web scraping and APIs to automatically collect necessary data from the Internet and other networks.

[0615] The "means for selecting appropriate insurance products" refers to a technology that enables a generative artificial intelligence model to find the most suitable insurance product based on the user's attribute information and medical history.

[0616] MODE FOR CARRYING OUT THE INVENTION

[0617] The present invention relates to a system that proposes optimal medical insurance products based on data such as user attribute information, medical history, insurance needs, etc. This system has the function of receiving user input information, collecting and analyzing information on medical insurance services using a generative artificial intelligence model, selecting the optimal insurance product, and presenting it to the user.

[0618] System Components

[0619] 1. User Interface (Terminal)

[0620] The terminal provides an interface for users to enter demographic information, medical history, and insurance requirements. This interface displays a form in which users can enter their age, gender, medical history, current medical history, and desired insurance conditions.

[0621] 2. Information receiving and storage means (server)

[0622] The server receives the information sent by the user, checks the content, and stores it in a database, making it possible to reliably manage individual user information.

[0623] 3. Data collection method (server)

[0624] The server uses a generative artificial intelligence model (e.g., GPT model) to automatically collect health insurance data from the network environment, using web scraping technology and APIs.

[0625] 4. Insurance product analysis and selection method (server)

[0626] The server analyzes the collected information on medical insurance services. The generative AI model selects the most suitable insurance product based on the information entered by the user. The analysis results are organized into a list of insurance products that meet the user's needs.

[0627] 5. Information provision means (terminal)

[0628] The terminal displays details of the insurance product provided by the server to the user, and if the user enters any additional questions, the terminal sends them to the server.

[0629] 6. Server for responding to additional questions

[0630] The server receives follow-up questions from the user and uses a generative artificial intelligence model to generate appropriate answers, which are then provided to the user in real time.

[0631] 7. Contact Information Providing Means (Server)

[0632] For questions that the generative artificial intelligence model cannot answer, the server lists appropriate contact information and provides it to the user.

[0633] Specific examples

[0634] For example, let's say a 30-year-old female user has a history of asthma, no current medical history, and wants medical insurance that provides generous coverage for hospitalization expenses. The user enters this information into the device and presses the send button. The server receives the information and uses a generative artificial intelligence model to collect publicly available medical insurance information. The collected data is organized in the following format:

[0635] Insurance A: Basic medical insurance, covers 80% of hospital costs

[0636] Insurance B: Covers 100% of hospital costs, but is expensive for people with asthma

[0637] Insurance C: A plan that covers 90% of hospitalization costs and is specifically designed for asthma.

[0638] The generative AI model analyzes this information and proposes the most suitable insurance C. The proposal, along with details, is presented to the user via their device. The user can then enter more detailed questions, and the server will use the generative AI model to instantly generate an answer. If the server cannot provide an answer, it will provide the appropriate contact information. This process allows users to easily find the medical insurance product that best suits them.

[0639] Prompt Sentence Examples

[0640] "I'm a 30-year-old woman with a medical history of asthma and no current medical conditions. I'm looking for coverage for hospitalization expenses. I'd like you to recommend the most suitable medical insurance product for me."

[0641] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0642] Step 1:

[0643] Users enter their age, gender, medical history, current medical history, and insurance needs into the terminal interface.

[0644] The information entered specifically includes data such as "Age: 30," "Gender: Female," "Medical history: Asthma," and "Insurance preference: Generous coverage for hospitalization expenses."

[0645] The terminal receives this information and temporarily stores it.

[0646] Step 2:

[0647] When the user presses the send button, the terminal sends the entered information to the server.

[0648] The server checks the received data and verifies that there are no omissions or errors.

[0649] The input is user information sent from the terminal.

[0650] The output is the verified user information saved in the database.

[0651] Step 3:

[0652] Based on the stored user information, the server uses a generative artificial intelligence model to collect information on medical insurance services from the network environment.

[0653] The input is user information and a collection prompt (e.g., "30 years old, female. Medical history of asthma, no current medical history. Prioritizing coverage for hospitalization expenses. Please suggest the most suitable medical insurance product.").

[0654] The output is collected health insurance service information data.

[0655] The data is collected using web scraping technology and APIs, specifically by retrieving the latest insurance plan information from insurance companies' official websites and public APIs.

[0656] Step 4:

[0657] The server analyzes the collected medical insurance service information using a generative artificial intelligence model.

[0658] The input is the insurance service data and user information collected in step 3.

[0659] The output is a list of insurance products that best suit the user's needs.

[0660] The generative artificial intelligence model used for the analysis is designed to select the most suitable insurance product based on the user's attribute information.

[0661] Step 5:

[0662] The server transmits the selected optimal insurance product list to the terminal.

[0663] The input is a list of the best insurance products.

[0664] The output is the insurance product details that are presented to the user.

[0665] The terminal displays the received information to the user.

[0666] As a specific example, details of "Insurance C: A plan that covers 90% of hospitalization costs and specializes in asthma" are displayed.

[0667] Step 6:

[0668] The user enters further detailed questions about the displayed insurance product into the terminal.

[0669] The terminal receives the input question and transmits it to the server.

[0670] The input is a follow-up question from the user (e.g., "Does Insurance C cover emergency transport costs?").

[0671] The output is query data to the server.

[0672] Step 7:

[0673] The server passes the received follow-up questions to a generative artificial intelligence model to generate appropriate answers.

[0674] The input is additional question data from the user.

[0675] The output is the generated answer.

[0676] The created answer is sent back to the terminal, which displays it to the user.

[0677] Step 8:

[0678] For questions that the generative artificial intelligence model cannot answer, the server lists appropriate contact points.

[0679] The input is question data that the generative artificial intelligence model cannot answer.

[0680] The output is a list of appropriate contacts.

[0681] This contact information is then sent back to the terminal, which provides it to the user.

[0682] (Application example 1)

[0683] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0684] Conventional medical insurance product proposal systems are limited to providing information on medical insurance products and are unable to fully meet the diverse needs of users. Furthermore, they do not propose optimal payment methods, including payment services, making it difficult for users to find the service that best suits them.

[0685] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0686] In this invention, the server includes means for receiving and storing information such as the user's age, gender, medical history, current medical history, payment history, purchase history, and insurance needs, means for automatically collecting information on medical insurance services and payment services from the Internet using a generative AI model, means for analyzing the collected information on medical insurance services and payment services based on the user's information and selecting the most appropriate insurance product and payment method, means for presenting the user with details of the most appropriate insurance product and payment method and generating answers to any additional questions, and means for presenting an appropriate contact point if the generative AI model cannot provide an answer. This makes it possible to propose optimal medical insurance products and payment methods that meet the diverse needs of users.

[0687] "User Information" refers to information such as the user's age, gender, medical history, current medical history, payment history, purchase history, and insurance requirements.

[0688] A "generative artificial intelligence model" refers to an artificial intelligence model that collects information from the Internet and makes optimal suggestions based on user information.

[0689] "Medical insurance services" refers to services that provide optimal medical insurance products based on the user's health condition and insurance needs.

[0690] "Payment services" refers to a means of providing optimal payment methods and discount services based on a user's payment history and purchase history.

[0691] "User interface" refers to the screens and forms through which users enter information and view system results.

[0692] "Web scraping" refers to the technique of automatically collecting information on the Internet.

[0693] "Contact Information" refers to the appropriate contact point for users to inquire about questions that the generative AI model cannot answer.

[0694] MODE FOR CARRYING OUT THE INVENTION

[0695] System Components

[0696] 1. User interface (smartphone app)

[0697] The user interface provides a form for users to enter their own information, including age, gender, medical history, current medical history, payment history, purchase history, and insurance needs. For example, by launching a smartphone app and entering information into the input form displayed on the screen, this data is sent to the system.

[0698] 2. Information receiving and storage means (cloud server)

[0699] The cloud server receives user information sent from the smartphone app and stores it in a database, where it is safely stored and can be accessed whenever necessary.

[0700] 3. Generative AI model (cloud server)

[0701] The cloud server uses a generative artificial intelligence model (e.g., GPT model) based on the stored user information to collect information on health insurance services and payment services from the internet. This includes using web scraping technology and APIs to quickly obtain the latest information. The collected data is organized on the cloud server and filtered in an optimal manner based on the user's needs.

[0702] 4. Insurance product analysis and selection

[0703] The cloud server analyzes the collected information on medical insurance services and payment services using a generative AI model. Based on the user information, it selects the most suitable insurance product and payment method, and prepares this selected information as data to present to the user.

[0704] 5. Information provision method (smartphone app)

[0705] The smartphone app displays detailed information on the optimal insurance product and payment method selected for the user. The user can then enter additional questions based on this information. The cloud server receives the additional questions and generates answers using a generative artificial intelligence model.

[0706] 6. Dealing with unanswerable questions

[0707] For questions that the generative AI model cannot answer, the cloud server will provide a list of appropriate contact points to the user, allowing them to quickly obtain the information they need.

[0708] Hardware and software used

[0709] The system is implemented using the following hardware and software:

[0710] Smartphone app (e.g. Android or iOS)

[0711] Cloud servers (e.g. AWS, Google Cloud, Microsoft Azure)

[0712] Databases (e.g. AWS RDS, Google Cloud SQL, Microsoft Azure SQL Database)

[0713] Generative AI models (e.g., OpenAI GPT-3, other AI models)

[0714] Web scraping technology (e.g., Beautiful Soup, Scrapy)

[0715] Specific examples

[0716] As a concrete example, consider a 30-year-old female user who has a medical history of asthma, no current medical history, spends a lot of money on groceries, and seeks generous coverage for hospital expenses. The user enters this information into an input form on a smartphone app and submits it. The cloud server receives the information and requests analysis from a generative artificial intelligence model. The AI ​​collects publicly available information on medical insurance and payment services, and lists the following insurance products and payment methods:

[0717] Insurance A: Basic medical insurance, covers 80% of hospital costs

[0718] Insurance B: Covers 100% of hospital costs, but is expensive for people with asthma

[0719] Insurance C: A plan that covers 90% of hospitalization costs and is specifically designed for asthma.

[0720] Payment Service A: Offers 5% cash back on grocery purchases

[0721] Payment Service B: Receive a 20% discount on grocery purchases over a certain amount each month

[0722] The generative AI model analyzes this information and recommends the most suitable insurance C and payment service A for the user. The recommendations, along with detailed benefits, are displayed on the smartphone app, providing peace of mind and savings opportunities for the user.

[0723] Prompt Sentence Examples

[0724] User Information:

[0725] Age: 30

[0726] Gender: Female

[0727] Payment history: Breakdown of monthly expenses (groceries 60%, eating out 20%, other 20%)

[0728] Insurance demand: cash back and discounts on groceries

[0729] Please suggest the best insurance product and payment service for this user."

[0730] The above is a specific embodiment for carrying out the invention, which allows users to receive proposals for optimal insurance products and payment methods that meet their diverse needs.

[0731] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0732] Step 1:

[0733] Input: The user enters information such as age, gender, medical history, current medical history, payment history, purchase history, and insurance requirements.

[0734] Action: A user uses a smartphone app to enter the required information into a form and presses the submit button.

[0735] Output: The entered user information is sent from the app to the server.

[0736] Step 2:

[0737] Input: Data containing user information is sent to the server.

[0738] How it works: The server analyzes the received user information and stores the data in a database.

[0739] Output: User information is saved in the database of the cloud server.

[0740] Step 3:

[0741] Input: A dataset is created based on the stored user information to be input into the generative artificial intelligence model.

[0742] How it works: The server uses a generative artificial intelligence model to collect information about health insurance and payment services from the internet. It obtains information using web scraping technology and APIs.

[0743] Output: The collected information on medical insurance services and payment services is sent to the server and organized.

[0744] Step 4:

[0745] Input: Collected and organized information on health insurance services and payment services is passed to the generative AI model.

[0746] How it works: The server analyzes the collected information using a generative artificial intelligence model and selects the most appropriate insurance product and payment method based on the user information.

[0747] Output: A list of optimal insurance products and payment methods is generated as a result of the analysis.

[0748] Step 5:

[0749] Input: A list of insurance products and payment methods is generated as a result of the analysis.

[0750] How it works: The server sends the generated list to the smartphone app.

[0751] Output: Detailed information on the best insurance products and payment methods for your smartphone app will be displayed.

[0752] Step 6:

[0753] Input: The user enters a follow-up question.

[0754] How it works: A user enters and submits a follow-up question through a smartphone app. The server uses a generative artificial intelligence model to generate an answer for the received question.

[0755] Output: The generated answers are displayed on the smartphone app.

[0756] Step 7:

[0757] Input: A question that the generative artificial intelligence model cannot answer is input.

[0758] How it works: The server lists appropriate contact points for users when they have questions that the generative AI model cannot answer, and provides them to them.

[0759] Output: A list of contacts is displayed on the smartphone app.

[0760] The above are the specific processing steps of this system's program. Through this series of processes, users can find the most suitable medical insurance product and payment method for them and obtain detailed information.

[0761] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0762] The system of the present invention is composed of a combination of software and hardware for implementing a series of processes to propose optimal medical insurance products based on user information. This system also includes an emotion engine that recognizes the user's emotions, enabling more personalized proposals and responses depending on the user's emotional state.

[0763] System Components

[0764] 1. User Interface (Terminal):

[0765] It displays a form for users to enter their information, including age, gender, medical history, current medical history, and insurance preferences.

[0766] Emotion data is acquired using a camera or microphone while the user is typing.

[0767] 2. Information receiving and storage means (server):

[0768] Receives user information and emotion data sent from the device and stores it in a database.

[0769] 3. Generative AI model (server):

[0770] A generative artificial intelligence model (e.g., GPT model) is used to collect information on health insurance services from the Internet based on user information.

[0771] Analyze insurance service information and select the insurance product that best suits the user's needs.

[0772] 4. Emotion engine (server):

[0773] The system recognizes emotions from user input and interactions, and reflects the results in insurance product suggestions and responses.

[0774] 5. Information provision means (terminal):

[0775] Provide users with detailed information on the most suitable insurance products.

[0776] Generate answers to follow-up questions from the user.

[0777] Present information and respond in different formats based on the user's emotional state.

[0778] 6. Contact information providing means (server):

[0779] For questions that the generative artificial intelligence model cannot answer, a list of appropriate contact points is provided to the user.

[0780] Explanation of program processing

[0781] Entering and saving user information

[0782] The user inputs their age, gender, medical history, current medical history, and insurance needs. The device receives this information and, upon pressing the send button, sends it to the server. During this process, the device's camera and microphone are used to capture the user's emotional data. The server checks the received information and emotional data and stores it in a database.

[0783] Health insurance service survey

[0784] The server uses a generative artificial intelligence model based on the stored user information to collect online health insurance service information, including through web scraping and API usage, and organizes and filters the collected data to best meet the user's needs.

[0785] Insurance product analysis and selection

[0786] The server analyzes the collected information on medical insurance services using a generative AI model and selects the most suitable insurance product based on the user's information and emotional data. This selected information is prepared as data to be presented to the user.

[0787] Utilizing Emotional Data

[0788] The emotion engine (server) analyzes the stored emotional data and determines whether the user is relaxed, stressed, etc. Based on this information, the generative AI model presents detailed information about insurance products to the user.

[0789] Presenting results and responding to follow-up questions

[0790] The terminal displays a list of selected optimal insurance products and their detailed information to the user. The user can then enter additional questions based on this information. The server receives the additional questions and requests a generative AI model to generate answers. Information is provided in an appropriate format based on the user's emotional state.

[0791] Dealing with unanswerable questions

[0792] For questions that the generative artificial intelligence model cannot answer, the server will provide the user with a list of appropriate contact points.

[0793] Specific examples

[0794] As a concrete example, let's say a 30-year-old female user has a medical history of asthma, no current medical history, and is seeking generous coverage for hospitalization expenses. The user enters this information into a terminal and sends it. The server receives the information and requests analysis from a generative AI model. The AI ​​collects publicly available medical insurance information and lists the following insurance products:

[0795] Insurance A: Basic medical insurance, covers 80% of hospital costs

[0796] Insurance B: Covers 100% of hospital costs, but is expensive for people with asthma

[0797] Insurance C: A plan that covers 90% of hospitalization costs and is specifically designed for asthma.

[0798] The generative AI model further analyzes this information and proposes the most suitable insurance C to the user. The emotion engine analyzes the user's emotions and presents the information in an appropriate format based on stress and sense of security. The proposal is presented to the user along with detailed benefits, providing peace of mind to the user.

[0799] The processing flow will be explained below.

[0800] Step 1:

[0801] The user accesses the terminal interface and inputs information such as age, sex, medical history, current medical history, and insurance requirements.

[0802] Step 2:

[0803] The device receives information input by the user and simultaneously acquires the user's emotional data using the device's camera and microphone.

[0804] Step 3:

[0805] When the terminal presses the send button, the user information and emotion data are sent to the server.

[0806] Step 4:

[0807] The server receives the information and emotion data sent by the user and stores them in a database.

[0808] Step 5:

[0809] Based on the stored user information, the server generates a request to the generative artificial intelligence model to investigate medical insurance services.

[0810] Step 6:

[0811] The server collects this information by performing web scraping and API requests to collect information about health insurance services on the Internet.

[0812] Step 7:

[0813] The server analyzes the medical insurance service information collected and compares it with user information to perform filtering.

[0814] Step 8:

[0815] The generative artificial intelligence model (server) uses the analytical data to select the insurance product that best suits the user's needs.

[0816] Step 9:

[0817] Based on the analysis results received by the server from the generative artificial intelligence model, the server generates detailed information and a proposal for the optimal insurance product.

[0818] Step 10:

[0819] The server uses an emotion engine to analyze the stored emotion data and determine the user's current emotional state.

[0820] Step 11:

[0821] Based on the analysis results from the emotion engine, the server optimizes detailed information and proposals for insurance products in a format that corresponds to the user's emotional state.

[0822] Step 12:

[0823] The terminal displays the optimized insurance product list and detailed information received from the server to the user.

[0824] Step 13:

[0825] The user checks the displayed insurance information, and if there are any additional questions, enters them in the input form and presses the submit button.

[0826] Step 14:

[0827] The server receives a follow-up question from the user and requests the generative artificial intelligence model to generate an answer.

[0828] Step 15:

[0829] The generative artificial intelligence model (server) generates optimal answers to follow-up questions from the user and sends them back to the server.

[0830] Step 16:

[0831] The server receives the response from the generative artificial intelligence model and converts it into a data format for display to the user.

[0832] Step 17:

[0833] The terminal displays the answer received from the server to the user.

[0834] Step 18:

[0835] For questions that the generative artificial intelligence model cannot answer, the server lists appropriate contact points and presents them to the user.

[0836] Example 2

[0837] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0838] Conventional medical insurance product recommendation systems have difficulty proposing optimal insurance products that meet the individual needs of users, and have a particular problem of lacking personalized recommendations based on emotional states. Another problem is that when the generative AI model cannot answer a question, it takes time and effort for the user to find the appropriate contact point.

[0839] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0840] In this invention, the server includes means for receiving and storing personal information such as the user's age, gender, medical history, current medical history, and insurance needs, means for automatically collecting information on medical insurance services from the Internet using a generative AI model, means for analyzing the collected information on medical insurance services based on the user's personal information and selecting the most suitable insurance product, means for acquiring the user's emotional data and customizing the insurance product proposal content and information provision format based on the data, means for presenting the user with details of the most suitable insurance product and generating answers to additional questions, and means for presenting an appropriate contact point if the generative AI model cannot provide an answer. This makes it possible to propose personalized insurance products based on emotions and quickly provide contact points for inquiries to the user.

[0841] "User's personal information" refers to information that is useful for identifying a user and proposing insurance products, such as the user's age, gender, medical history, current medical history, and insurance needs.

[0842] A "generative AI model" refers to a model that uses artificial intelligence technology to collect information from the internet and generate data based on that information.

[0843] "Means of automatically collecting information on health insurance services from the Internet" refers to the process of automatically collecting information on health insurance services that are publicly available on the Internet using technologies such as web scraping and the use of APIs.

[0844] "Means for acquiring emotional data" refers to technology that uses hardware such as cameras and microphones to collect the user's facial expressions and voice, and then analyzes the user's emotional state based on this.

[0845] "Means for selecting the most suitable insurance product" refers to technology that analyzes the user's personal information and collected information on medical insurance services to select the insurance product that is most suitable for the user.

[0846] "Means for customizing the content of suggestions and the format of information provided" refers to the process of adjusting and customizing the content and format of information provided to users based on the acquired emotional data.

[0847] "Means for providing appropriate contact information" refers to technology for providing relevant contact information to the user when the generative AI model is unable to answer the user's follow-up question.

[0848] The system of the present invention is comprised of a combination of software and hardware for implementing a series of processes to recommend optimal health insurance products based on user information. The system includes an emotion engine that recognizes the user's emotional state and makes personalized recommendations accordingly.

[0849] System Components

[0850] 1. User Interface (Terminal):

[0851] It displays a form for users to enter their information, including age, gender, medical history, current medical history, and insurance preferences.

[0852] Emotion data is acquired using a camera or microphone while the user is typing.

[0853] 2. Information receiving and storage means (server):

[0854] Receives user information and emotion data sent from the device and stores it in a database.

[0855] 3. Generative AI model (server):

[0856] A generative artificial intelligence model (e.g., GPT model) is used to collect information on health insurance services from the Internet based on user information.

[0857] Analyze insurance service information and select the insurance product that best suits the user's needs.

[0858] 4. Emotion engine (server):

[0859] The system recognizes emotions from user input and interactions, and reflects the results in insurance product suggestions and responses.

[0860] 5. Information provision means (terminal):

[0861] Provide users with detailed information on the most suitable insurance products.

[0862] Generate answers to follow-up questions from the user.

[0863] Present information and respond in different formats based on the user's emotional state.

[0864] 6. Contact information providing means (server):

[0865] For questions that the generative AI model cannot answer, a list of appropriate contact points is provided to the user.

[0866] Explanation of program processing

[0867] Entering and saving user information

[0868] The user inputs their age, gender, medical history, current medical history, and insurance needs on the device. The device receives this information, and when the user presses the send button, emotional data obtained using the camera and microphone is sent to the server along with the information.

[0869] Collection of medical insurance service information

[0870] The server uses stored user information to gather health insurance service information from the internet using a generative artificial intelligence model, including web scraping and API usage.

[0871] Insurance product analysis and selection

[0872] The server uses a generative AI model to analyze the collected information on medical insurance services. Based on user information and emotional data, it selects the most suitable insurance product and organizes the proposal.

[0873] Utilizing Emotional Data

[0874] The emotion engine (server) analyzes the stored emotion data and determines whether the user is relaxed or stressed. Based on this result, it customizes the format and content of the information provided.

[0875] Presenting optimal insurance products and responding to additional questions

[0876] The terminal displays a list of the best insurance products and their details to the user. The information provided to the user is presented in an appropriate format based on their emotional state. The user can then enter additional questions based on the information presented.

[0877] Responding to any additional questions and providing contact information

[0878] The server receives the user's follow-up questions and asks the generative AI model to generate an answer. If the generative AI model cannot answer the question, the server lists appropriate contact points and provides them to the user.

[0879] Specific examples

[0880] As a concrete example, let's say a 30-year-old female user has a medical history of asthma, no current medical history, and is seeking generous coverage for hospitalization expenses. The user enters this information into a terminal and sends it. The server receives the information and requests analysis from a generative AI model. The generative AI collects publicly available medical insurance information and lists the following insurance products:

[0881] Insurance A: Basic medical insurance, covers 80% of hospital costs

[0882] Insurance B: Covers 100% of hospital costs, but is expensive for people with asthma

[0883] Insurance C: A plan that covers 90% of hospitalization costs and is specifically designed for asthma.

[0884] The server further analyzes this information and proposes the most suitable insurance C to the user. The emotion engine analyzes the user's emotions and presents the information in an appropriate format based on stress and sense of security. The proposal is presented to the user along with detailed benefits, providing peace of mind to the user.

[0885] Example of input prompt for the generative AI model to be used

[0886] "I'm a 30-year-old woman with a history of asthma and no current medical conditions. I'm looking for medical insurance that provides comprehensive coverage for hospitalization costs. Which insurance would be best for me?"

[0887] This system automatically suggests the most suitable medical insurance product based on the user's input information and emotional data, and provides personalized responses based on emotions, making it easier for users to find the insurance product that is most suitable for them.

[0888] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0889] Program processing steps

[0890] Step 1: Enter user information and obtain emotion data

[0891] The user enters their age, gender, medical history, current medical history, and insurance needs into the input form on the terminal. The input information is received by the terminal.

[0892] Input: Personal information entered by the user (e.g., age, gender, medical history, current medical history, insurance requests)

[0893] Output: Received user information and emotion data

[0894] Specific operation: The user enters information into the form, such as "30 years old, female, with asthma, looking for insurance that covers hospitalization costs generously." The device then activates the camera to capture the user's facial expressions and uses the microphone to obtain voice data.

[0895] Step 2: Send and store user information and emotion data

[0896] The device sends the acquired user information and emotion data to the server, which then stores the received information in a database.

[0897] Input: Received user information and emotion data

[0898] Output: User information and emotion data stored in a database

[0899] Specific operation: The device sends information such as "Age: 30, Gender: Female, Medical history: Asthma, Current medical history: None, Request: Coverage of hospital expenses, Emotional data: Stress level 3, Peace of mind level 2" to the server, which then stores it in a database.

[0900] Step 3: Collect health insurance service information

[0901] The server uses a generative artificial intelligence model based on the stored user information to gather information about health insurance services from the internet, including through web scraping and API usage.

[0902] Input: User information stored in the database

[0903] Output: Collected health insurance service information

[0904] Specific operation: The server inputs the following prompt sentence into the generative AI model: "30 years old, female, with a history of asthma, looking for medical insurance that prioritizes hospitalization costs. Please give me a list of recommended insurance products." The generative AI model collects medical insurance information from the web and returns a list of multiple insurance products such as "Insurance A, Insurance B, Insurance C."

[0905] Step 4: Insurance product analysis and selection

[0906] The server analyzes the collected information on medical insurance services using a generative AI model, and selects the most suitable insurance product based on user information and emotional data.

[0907] Input: Collected medical insurance service information, user information, and emotion data

[0908] Output: Recommendations for optimal insurance products

[0909] Specific operation: The server selects Insurance C, which best suits the user's needs, from the collected information: Insurance A (80% coverage), Insurance B (100% coverage, high asthma premium), and Insurance C (90% coverage). The selected information is organized into a detailed proposal.

[0910] Step 5: Analyze emotional data and customize information

[0911] The emotion engine (server) analyzes the stored emotion data to determine the user's emotional state, and customizes the information presentation format based on this result.

[0912] Input: Emotion data

[0913] Output: customized information format

[0914] Specific operation: The emotion engine (server) determines from the emotion data that the user's stress level is high and generates a detailed and polite explanation.

[0915] Step 6: Present the best insurance product and receive additional questions

[0916] The terminal displays a list of the best insurance products and their details to the user, who can then enter additional questions based on the information displayed.

[0917] Input: Recommendation for the best insurance product

[0918] Output: Insurance product details presented to the user

[0919] What happens: The device displays detailed information such as "Insurance C: A plan that covers 90% of hospitalization costs. Specializes in asthma, monthly premium is 8000 yen." The user then enters an additional question: "What benefits does Insurance C offer?"

[0920] Step 7: Generate answers to follow-up questions

[0921] The server receives the user's follow-up question, asks the generative AI model to generate an answer, and presents the generated answer to the user.

[0922] Input: User's additional question

[0923] Output: The generated answer

[0924] Specific operation: The server inputs "Additional question from the user: What benefits does Insurance C offer?" into the generative AI model, obtains an answer from the generative AI model such as "Insurance C offers benefits such as transportation subsidies for hospitalization and online consultations with a dedicated doctor," and presents this to the user.

[0925] Step 8: Provide contact information for unanswered questions

[0926] For questions that the generative AI model cannot answer, the server lists appropriate contact points and provides them to the user.

[0927] Input: Notification that the generative AI model is unable to answer the question

[0928] Output: Appropriate contact information

[0929] Specific operation: The server investigates questions that the generative AI model cannot answer and provides the user with information such as, "For more information, please contact the following: Insurance Company C, Inquiry Number: XXX-XXXX-XXXX."

[0930] (Application example 2)

[0931] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0932] Conventional medical insurance product recommendation systems make recommendations without taking the user's emotional state into consideration, which can make it difficult for users to understand and accept them with a sense of security. While conventional systems can collect information and make recommendations using generative artificial intelligence models, they lack personalization using emotional data. When a user is stressed or not relaxed, it is difficult to provide appropriate information, and there is a need to improve user satisfaction.

[0933] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0934] In this invention, the server includes means for receiving and storing information such as the user's age, gender, medical history, current medical history, and insurance needs, means including an emotion engine for recognizing the user's emotions, means for automatically collecting information on medical insurance services from the Internet using a generative artificial intelligence model, means for analyzing the collected information on medical insurance services based on the user's information and emotional data and selecting an optimal insurance product, means for presenting details of the optimal insurance product in a format appropriate to the user's emotional state and generating answers to follow-up questions, and means for presenting an appropriate contact point if the generative artificial intelligence model cannot provide an answer. This makes it possible to propose individual insurance products according to the user's emotional state and to propose information that the user can accept with peace of mind.

[0935] "User information" refers to personal information entered by the user, such as age, gender, medical history, current medical history, and insurance requests.

[0936] An "emotion engine" is a system or software for recognizing user emotions and analyzing that data.

[0937] A "generative artificial intelligence model" is a system that includes machine learning algorithms to generate appropriate information and make suggestions based on user input.

[0938] A "user interface" is a system or screen configuration that provides a screen or form for a user to enter information.

[0939] "Emotional data" refers to emotional data based on facial expressions, tone of voice, body movements, etc., captured using the user's camera or microphone.

[0940] "Web scraping" is a technical method for automatically collecting data from the Internet.

[0941] "Insurance service information" refers to information about the most suitable medical insurance products that meet the user's needs.

[0942] "Contact Information" refers to the appropriate contact point for the user to obtain additional information or assistance if the generative AI model is unable to provide an answer.

[0943] "Pre-selection" is the process of selecting the most suitable medical insurance product based on user information.

[0944] System Configuration

[0945] The system according to the present invention comprises the following elements:

[0946] 1. User Interface (Terminal):

[0947] It displays a form for the user to enter their information, including age, gender, medical history, current medical history, and insurance preferences.

[0948] Emotion data is acquired using a camera or microphone while the user is typing.

[0949] 2. Information receiving and storage means (server):

[0950] Receives user information and emotion data sent from the device and stores it in a database.

[0951] 3. Generative AI model (server):

[0952] A generative artificial intelligence model is used to collect information on medical insurance services from the Internet based on user information.

[0953] Analyze insurance service information and select the insurance product that best suits the user's needs.

[0954] 4. Emotion engine (server):

[0955] The system recognizes emotions from user input and interactions, and reflects the results in insurance product suggestions and responses.

[0956] 5. Information provision means (terminal):

[0957] Provide users with detailed information on the most suitable insurance products.

[0958] Generate answers to follow-up questions from the user.

[0959] Present information and respond in different formats based on the user's emotional state.

[0960] 6. Contact information providing means (server):

[0961] For questions that the generative artificial intelligence model cannot answer, a list of appropriate contact points is provided to the user.

[0962] Program processing

[0963] The server receives the user's age, gender, medical history, current medical history, and insurance needs through the device when the user enters them, as well as emotional data captured using the camera and microphone when the user enters the information, and stores this in a database.

[0964] The generative AI model automatically collects information about health insurance services from the internet based on the received user information. This information is collected using web scraping technology. The collected data is analyzed, and the optimal insurance product is selected taking into account the user's information and emotional data.

[0965] The emotion engine analyzes the stored emotional data to determine whether the user is relaxed, stressed, etc. Based on this emotional information, the generative AI model selects detailed information about insurance products and presents them to the user in an appropriate format.

[0966] The terminal displays a list of selected optimal insurance products and their detailed information to the user. The user can then enter additional questions based on this information. The server receives the additional questions and requests the generative AI model to generate an answer. Information is provided in an appropriate format based on the user's emotional state. For questions that the generative AI model cannot answer, the server lists appropriate contact points and provides them to the user.

[0967] Specific examples

[0968] As a concrete example, consider a 30-year-old female user with a medical history of asthma, no current medical history, and a desire for generous coverage for hospitalization expenses. The user enters this information into a terminal and sends it. The server receives the information and requests analysis from a generative AI model. The AI ​​collects publicly available medical insurance information and lists the following insurance products, for example:

[0969] Insurance A: Basic medical insurance, covers 80% of hospital costs

[0970] Insurance B: Covers 100% of hospital costs, but is expensive for people with asthma

[0971] Insurance C: A plan that covers 90% of hospitalization costs and is specifically designed for asthma.

[0972] The generative AI model further analyzes this information and proposes the most suitable insurance C to the user. The emotion engine analyzes the user's emotions, and the information is presented in an appropriate format based on stress and sense of security.

[0973] Example prompts to input to the generative AI model

[0974] User info:

[0975] Age: 30

[0976] Gender: Female

[0977] Medical history: Asthma

[0978] Current medical status: None

[0979] Insurance needs: Comprehensive hospitalization coverage

[0980] User emotion analysis:

[0981] Current emotion: Nervous

[0982] Based on the above information and current emotion, what is the best health insurance plan for this user? Provide detailed plan options.

[0983] This prompt is then fed into a generative AI model, which then suggests appropriate insurance products in real time and can flexibly respond to changes in the user's emotions.

[0984] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0985] Specific explanations divided into processing steps

[0986] Step 1:

[0987] Entering and saving user information

[0988] The user enters their age, gender, medical history, current medical history, and insurance needs. The device receives this information and, upon pressing the send button, sends it to the server. During this process, the device's camera and microphone are used to acquire the user's emotional data. The input is the user's personal information and emotional data, and the output is sent to the server. Data processing involves converting and saving the user information and emotional data in a format with metadata.

[0989] Step 2:

[0990] Store user information and emotional data

[0991] The server receives user information and emotion data sent from the device. The received information is stored in a database. User information and emotion data are sent to the server as input, and this information is stored in a database as output. Data processing involves normalizing and verifying the received information.

[0992] Step 3:

[0993] Gathering information on medical insurance services

[0994] The server uses a generative artificial intelligence model based on the stored user information to collect online health insurance service information. Web scraping technology is used for collection. User information and website URLs are input, and health insurance service data is obtained as output. Data processing involves structuring the collected data and filtering out information relevant to the user.

[0995] Step 4:

[0996] Analysis and selection of medical insurance service information

[0997] The server analyzes the collected medical insurance service information using a generative AI model and selects the most suitable insurance product based on user information and emotional data. The input is medical insurance service information and user information, and the output is a list of the most suitable insurance products. In data processing, the emotional data is linked to the user's needs, and the generative AI model selects the appropriate insurance product.

[0998] Step 5:

[0999] Emotion data analysis and results presentation

[1000] The emotion engine analyzes the stored emotion data and determines whether the user is relaxed or stressed. Based on this information, the server presents the user with detailed information on the most suitable insurance products. The input is emotion data and a list of the most suitable insurance products, and the output is a customized presentation format and information content. Specifically, if the user is feeling stressed, softer language and explanation methods are used.

[1001] Step 6:

[1002] Generate answers to additional questions

[1003] The user can enter additional questions based on the insurance product presented. The device receives these and sends them to the server. The server then requests the generative AI model to generate an answer. The input is the additional question and related user information, and the output is the generated answer, which is displayed on the device. In terms of data calculation, the generative AI model generates an appropriate answer from a number of information sources.

[1004] Step 7:

[1005] Providing contact information

[1006] For questions that the generative AI model cannot answer, the server lists appropriate contact points and provides them to the user. The input is the unanswerable question and user information, and the output is the contact information displayed to the user. Data processing involves searching the knowledge base of the generative AI model for the most appropriate contact point.

[1007] Specific example processing explanation

[1008] For example, when a 30-year-old female user seeks insurance advice, she enters information into the device and uses a camera to collect facial expression data. The server receives and stores this data, then uses a generative AI model to select the most suitable insurance product. An emotion engine then analyzes the user's emotions and presents information based on the stress the user is feeling. Finally, the generative AI model suggests insurance products and responds to any follow-up questions.

[1009] Example prompts to input to a generative AI model:

[1010] User info:

[1011] Age: 30

[1012] Gender: Female

[1013] Medical history: Asthma

[1014] Current medical status: None

[1015] Insurance needs: Comprehensive hospitalization coverage

[1016] User emotion analysis:

[1017] Current emotion: Nervous

[1018] Based on the above information and current emotion, what is the best health insurance plan for this user? Provide detailed plan options.

[1019] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1020] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1021] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1022] [Third embodiment]

[1023] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1024] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1025] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1026] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1027] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1028] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1029] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1030] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1031] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1032] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1033] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1034] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1035] The system of the present invention is composed of a combination of software and hardware for implementing a series of processes to propose optimal medical insurance products based on user information. This system provides an interface for inputting the user's age, gender, medical history, current medical history, insurance needs, etc., and then uses a generative artificial intelligence model to research and analyze medical insurance services and propose optimal insurance products to the user.

[1036] System Components

[1037] 1. User Interface (Terminal):

[1038] It displays a form for users to enter their information, including age, gender, medical history, current medical history, and insurance preferences.

[1039] 2. Information receiving and storage means (server):

[1040] Receives user information sent from the device and stores it in a database.

[1041] 3. Generative AI model (server):

[1042] A generative artificial intelligence model (e.g., GPT model) is used to collect information on health insurance services from the Internet based on user information.

[1043] Analyze insurance service information and select the insurance product that best suits the user's needs.

[1044] 4. Information provision means (terminal):

[1045] Provide users with detailed information on the most suitable insurance products.

[1046] Generate answers to follow-up questions from the user.

[1047] 5. Contact information providing means (server):

[1048] For questions that the generative artificial intelligence model cannot answer, a list of appropriate contact points is provided to the user.

[1049] Explanation of program processing

[1050] Entering and saving user information

[1051] The user enters their age, gender, medical history, current medical history, and insurance needs. The device receives this information and, when they press the send button, it is sent to the server. The server verifies the received information and saves it in a database.

[1052] Health insurance service survey

[1053] The server uses a generative artificial intelligence model based on the stored user information to collect online health insurance service information, including through web scraping and API usage, and organizes and filters the collected data to best meet the user's needs.

[1054] Insurance product analysis and selection

[1055] The server analyzes the collected information on medical insurance services using a generative AI model and selects the most suitable insurance product based on the user information. This selected information is prepared as data to be presented to the user.

[1056] Presenting results and responding to follow-up questions

[1057] The terminal displays a list of the best insurance products and their details to the user. The user can then enter additional questions based on the list. The server receives the additional questions and generates answers using a generative AI model.

[1058] Dealing with unanswerable questions

[1059] For questions that the generative artificial intelligence model cannot answer, the server will provide the user with a list of appropriate contact points.

[1060] Specific examples

[1061] As a concrete example, let's say a 30-year-old female user has a medical history of asthma, no current medical history, and is seeking generous coverage for hospitalization expenses. The user enters this information into a terminal and sends it. The server receives the information and requests analysis from a generative AI model. The AI ​​collects publicly available medical insurance information and lists the following insurance products:

[1062] Insurance A: Basic medical insurance, covers 80% of hospital costs

[1063] Insurance B: Covers 100% of hospital costs, but is expensive for people with asthma

[1064] Insurance C: A plan that covers 90% of hospitalization costs and is specifically designed for asthma.

[1065] The generative AI model further analyzes this information and proposes the most suitable insurance C to the user. The proposal is presented to the user along with detailed benefits, providing peace of mind to the user.

[1066] The processing flow will be explained below.

[1067] Step 1:

[1068] The user accesses the terminal interface and inputs information such as age, sex, medical history, current medical history, and insurance requirements.

[1069] Step 2:

[1070] The terminal receives information entered by the user, and when the send button is pressed, the information is sent to the server.

[1071] Step 3:

[1072] The server receives the user information sent from the terminal, verifies the information, and then stores it in a database.

[1073] Step 4:

[1074] Based on the stored user information, the server generates a request to the generative artificial intelligence model to investigate medical insurance services.

[1075] Step 5:

[1076] The server collects this information by performing web scraping and API requests to collect information about health insurance services on the Internet.

[1077] Step 6:

[1078] The server analyzes the medical insurance service information collected and compares it with user information to perform filtering.

[1079] Step 7:

[1080] The generative artificial intelligence model (server) uses the analytical data to select the insurance product that best suits the user's needs.

[1081] Step 8:

[1082] Based on the analysis results received by the server from the generative artificial intelligence model, the server generates detailed information and a proposal for the optimal insurance product.

[1083] Step 9:

[1084] The terminal displays the optimum insurance product list and detailed information received from the server to the user.

[1085] Step 10:

[1086] The user checks the displayed insurance information, and if there are any additional questions, enters them in the input form and presses the submit button.

[1087] Step 11:

[1088] The server receives a follow-up question from the user and requests the generative artificial intelligence model to generate an answer.

[1089] Step 12:

[1090] The generative artificial intelligence model (server) generates optimal answers to follow-up questions from the user and sends them back to the server.

[1091] Step 13:

[1092] The server receives the response from the generative artificial intelligence model and converts it into a data format for display to the user.

[1093] Step 14:

[1094] The terminal displays the answer received from the server to the user.

[1095] Step 15:

[1096] For questions that the generative artificial intelligence model cannot answer, the server lists appropriate contact points and presents them to the user.

[1097] Example 1

[1098] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1099] Selecting medical insurance is extremely complex, and it takes time and money for users to find the best insurance product for them. It is also difficult to accurately select an insurance product that meets the needs of each individual user. Furthermore, there is a need for a system that can respond quickly and accurately to additional questions and detailed inquiries. A new system is needed to solve these problems.

[1100] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1101] In this invention, the server includes a means for receiving and storing data on a user's attributes, medical history, and insurance needs; a means for automatically collecting medical insurance service data from a network environment using a generative artificial intelligence model; and a means for analyzing the collected medical insurance service data based on the user's information and selecting an appropriate insurance product. This allows users to easily find the medical insurance product that best suits them. Furthermore, user convenience can be enhanced by providing prompt answers to follow-up questions and appropriate contact information when the generative artificial intelligence model cannot respond.

[1102] "User demographic information" means data regarding a user's age, gender, medical history, current medical history, and specific insurance needs and requirements.

[1103] "Medical history" refers to records of illnesses and treatments a user has experienced in the past.

[1104] A "generative artificial intelligence model" is a program that uses artificial intelligence technology to process natural language, gather information, and analyze data.

[1105] A "network environment" is an information technology infrastructure over which data communication takes place, such as the Internet or an internal network.

[1106] "Health insurance services" are insurance products and plans offered by insurance companies to cover medical expenses and treatment costs.

[1107] "Collection" is the act of gathering data or information for a specific purpose.

[1108] "Analysis" is the process of finding specific patterns and trends based on collected data and drawing evaluations and conclusions.

[1109] "Selection" is the act of choosing the most appropriate option from multiple options.

[1110] "Means for receiving and storing attribute information, medical history, and insurance request data" refers to technology that allows a server to receive and securely store information entered by a user through a terminal.

[1111] "Means for automatically collecting data on health insurance services from a network environment" refers to technology that uses web scraping and APIs to automatically collect necessary data from the Internet and other networks.

[1112] The "means for selecting appropriate insurance products" refers to a technology that enables a generative artificial intelligence model to find the most suitable insurance product based on the user's attribute information and medical history.

[1113] MODE FOR CARRYING OUT THE INVENTION

[1114] The present invention relates to a system that proposes optimal medical insurance products based on data such as user attribute information, medical history, insurance needs, etc. This system has the function of receiving user input information, collecting and analyzing information on medical insurance services using a generative artificial intelligence model, selecting the optimal insurance product, and presenting it to the user.

[1115] System Components

[1116] 1. User Interface (Terminal)

[1117] The terminal provides an interface for users to enter demographic information, medical history, and insurance requirements. This interface displays a form in which users can enter their age, gender, medical history, current medical history, and desired insurance conditions.

[1118] 2. Information receiving and storage means (server)

[1119] The server receives the information sent by the user, checks the content, and stores it in a database, making it possible to reliably manage individual user information.

[1120] 3. Data collection method (server)

[1121] The server uses a generative artificial intelligence model (e.g., GPT model) to automatically collect health insurance data from the network environment, using web scraping technology and APIs.

[1122] 4. Insurance product analysis and selection method (server)

[1123] The server analyzes the collected information on medical insurance services. The generative AI model selects the most suitable insurance product based on the information entered by the user. The analysis results are organized into a list of insurance products that meet the user's needs.

[1124] 5. Information provision means (terminal)

[1125] The terminal displays details of the insurance product provided by the server to the user, and if the user enters any additional questions, the terminal sends them to the server.

[1126] 6. Server for responding to additional questions

[1127] The server receives follow-up questions from the user and uses a generative artificial intelligence model to generate appropriate answers, which are then provided to the user in real time.

[1128] 7. Contact Information Providing Means (Server)

[1129] For questions that the generative artificial intelligence model cannot answer, the server lists appropriate contact information and provides it to the user.

[1130] Specific examples

[1131] For example, let's say a 30-year-old female user has a history of asthma, no current medical history, and wants medical insurance that provides generous coverage for hospitalization expenses. The user enters this information into the device and presses the send button. The server receives the information and uses a generative artificial intelligence model to collect publicly available medical insurance information. The collected data is organized in the following format:

[1132] Insurance A: Basic medical insurance, covers 80% of hospital costs

[1133] Insurance B: Covers 100% of hospital costs, but is expensive for people with asthma

[1134] Insurance C: A plan that covers 90% of hospitalization costs and is specifically designed for asthma.

[1135] The generative AI model analyzes this information and proposes the most suitable insurance C. The proposal, along with details, is presented to the user via their device. The user can then enter more detailed questions, and the server will use the generative AI model to instantly generate an answer. If the server cannot provide an answer, it will provide the appropriate contact information. This process allows users to easily find the medical insurance product that best suits them.

[1136] Prompt Sentence Examples

[1137] "I'm a 30-year-old woman with a medical history of asthma and no current medical conditions. I'm looking for coverage for hospitalization expenses. I'd like you to recommend the most suitable medical insurance product for me."

[1138] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1139] Step 1:

[1140] Users enter their age, gender, medical history, current medical history, and insurance needs into the terminal interface.

[1141] The information entered specifically includes data such as "Age: 30," "Gender: Female," "Medical history: Asthma," and "Insurance preference: Generous coverage for hospitalization expenses."

[1142] The terminal receives this information and temporarily stores it.

[1143] Step 2:

[1144] When the user presses the send button, the terminal sends the entered information to the server.

[1145] The server checks the received data and verifies that there are no omissions or errors.

[1146] The input is user information sent from the terminal.

[1147] The output is the verified user information saved in the database.

[1148] Step 3:

[1149] Based on the stored user information, the server uses a generative artificial intelligence model to collect information on medical insurance services from the network environment.

[1150] The input is user information and a collection prompt (e.g., "30 years old, female. Medical history of asthma, no current medical history. Prioritizing coverage for hospitalization expenses. Please suggest the most suitable medical insurance product.").

[1151] The output is collected health insurance service information data.

[1152] The data is collected using web scraping technology and APIs, specifically by retrieving the latest insurance plan information from insurance companies' official websites and public APIs.

[1153] Step 4:

[1154] The server analyzes the collected medical insurance service information using a generative artificial intelligence model.

[1155] The input is the insurance service data and user information collected in step 3.

[1156] The output is a list of insurance products that best suit the user's needs.

[1157] The generative artificial intelligence model used for the analysis is designed to select the most suitable insurance product based on the user's attribute information.

[1158] Step 5:

[1159] The server transmits the selected optimal insurance product list to the terminal.

[1160] The input is a list of the best insurance products.

[1161] The output is the insurance product details that are presented to the user.

[1162] The terminal displays the received information to the user.

[1163] As a specific example, details of "Insurance C: A plan that covers 90% of hospitalization costs and specializes in asthma" are displayed.

[1164] Step 6:

[1165] The user enters further detailed questions about the displayed insurance product into the terminal.

[1166] The terminal receives the input question and transmits it to the server.

[1167] The input is a follow-up question from the user (e.g., "Does Insurance C cover emergency transport costs?").

[1168] The output is query data to the server.

[1169] Step 7:

[1170] The server passes the received follow-up questions to a generative artificial intelligence model to generate appropriate answers.

[1171] The input is additional question data from the user.

[1172] The output is the generated answer.

[1173] The created answer is sent back to the terminal, which displays it to the user.

[1174] Step 8:

[1175] For questions that the generative artificial intelligence model cannot answer, the server lists appropriate contact points.

[1176] The input is question data that the generative artificial intelligence model cannot answer.

[1177] The output is a list of appropriate contacts.

[1178] This contact information is then sent back to the terminal, which provides it to the user.

[1179] (Application example 1)

[1180] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1181] Conventional medical insurance product proposal systems are limited to providing information on medical insurance products and are unable to fully meet the diverse needs of users. Furthermore, they do not propose optimal payment methods, including payment services, making it difficult for users to find the service that best suits them.

[1182] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1183] In this invention, the server includes means for receiving and storing information such as the user's age, gender, medical history, current medical history, payment history, purchase history, and insurance needs, means for automatically collecting information on medical insurance services and payment services from the Internet using a generative AI model, means for analyzing the collected information on medical insurance services and payment services based on the user's information and selecting the most appropriate insurance product and payment method, means for presenting the user with details of the most appropriate insurance product and payment method and generating answers to any additional questions, and means for presenting an appropriate contact point if the generative AI model cannot provide an answer. This makes it possible to propose optimal medical insurance products and payment methods that meet the diverse needs of users.

[1184] "User Information" refers to information such as the user's age, gender, medical history, current medical history, payment history, purchase history, and insurance requirements.

[1185] A "generative artificial intelligence model" refers to an artificial intelligence model that collects information from the Internet and makes optimal suggestions based on user information.

[1186] "Medical insurance services" refers to services that provide optimal medical insurance products based on the user's health condition and insurance needs.

[1187] "Payment services" refers to a means of providing optimal payment methods and discount services based on a user's payment history and purchase history.

[1188] "User interface" refers to the screens and forms through which users enter information and view system results.

[1189] "Web scraping" refers to the technique of automatically collecting information on the Internet.

[1190] "Contact Information" refers to the appropriate contact point for users to inquire about questions that the generative AI model cannot answer.

[1191] MODE FOR CARRYING OUT THE INVENTION

[1192] System Components

[1193] 1. User interface (smartphone app)

[1194] The user interface provides a form for users to enter their own information, including age, gender, medical history, current medical history, payment history, purchase history, and insurance needs. For example, by launching a smartphone app and entering information into the input form displayed on the screen, this data is sent to the system.

[1195] 2. Information receiving and storage means (cloud server)

[1196] The cloud server receives user information sent from the smartphone app and stores it in a database, where it is safely stored and can be accessed whenever necessary.

[1197] 3. Generative AI model (cloud server)

[1198] The cloud server uses a generative artificial intelligence model (e.g., GPT model) based on the stored user information to collect information on health insurance services and payment services from the internet. This includes using web scraping technology and APIs to quickly obtain the latest information. The collected data is organized on the cloud server and filtered in an optimal manner based on the user's needs.

[1199] 4. Insurance product analysis and selection

[1200] The cloud server analyzes the collected information on medical insurance services and payment services using a generative AI model. Based on the user information, it selects the most suitable insurance product and payment method, and prepares this selected information as data to present to the user.

[1201] 5. Information provision method (smartphone app)

[1202] The smartphone app displays detailed information on the optimal insurance product and payment method selected for the user. The user can then enter additional questions based on this information. The cloud server receives the additional questions and generates answers using a generative artificial intelligence model.

[1203] 6. Dealing with unanswerable questions

[1204] For questions that the generative AI model cannot answer, the cloud server will provide a list of appropriate contact points to the user, allowing them to quickly obtain the information they need.

[1205] Hardware and software used

[1206] The system is implemented using the following hardware and software:

[1207] Smartphone app (e.g. Android or iOS)

[1208] Cloud servers (e.g. AWS, Google Cloud, Microsoft Azure)

[1209] Databases (e.g. AWS RDS, Google Cloud SQL, Microsoft Azure SQL Database)

[1210] Generative AI models (e.g., OpenAI GPT-3, other AI models)

[1211] Web scraping technology (e.g., Beautiful Soup, Scrapy)

[1212] Specific examples

[1213] As a concrete example, consider a 30-year-old female user who has a medical history of asthma, no current medical history, spends a lot of money on groceries, and seeks generous coverage for hospital expenses. The user enters this information into an input form on a smartphone app and submits it. The cloud server receives the information and requests analysis from a generative artificial intelligence model. The AI ​​collects publicly available information on medical insurance and payment services, and lists the following insurance products and payment methods:

[1214] Insurance A: Basic medical insurance, covers 80% of hospital costs

[1215] Insurance B: Covers 100% of hospital costs, but is expensive for people with asthma

[1216] Insurance C: A plan that covers 90% of hospitalization costs and is specifically designed for asthma.

[1217] Payment Service A: Offers 5% cash back on grocery purchases

[1218] Payment Service B: Receive a 20% discount on grocery purchases over a certain amount each month

[1219] The generative AI model analyzes this information and recommends the most suitable insurance C and payment service A for the user. The recommendations, along with detailed benefits, are displayed on the smartphone app, providing peace of mind and savings opportunities for the user.

[1220] Prompt Sentence Examples

[1221] User Information:

[1222] Age: 30

[1223] Gender: Female

[1224] Payment history: Breakdown of monthly expenses (groceries 60%, eating out 20%, other 20%)

[1225] Insurance demand: cash back and discounts on groceries

[1226] Please suggest the best insurance product and payment service for this user."

[1227] The above is a specific embodiment for carrying out the invention, which allows users to receive proposals for optimal insurance products and payment methods that meet their diverse needs.

[1228] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1229] Step 1:

[1230] Input: The user enters information such as age, gender, medical history, current medical history, payment history, purchase history, and insurance requirements.

[1231] Action: A user uses a smartphone app to enter the required information into a form and presses the submit button.

[1232] Output: The entered user information is sent from the app to the server.

[1233] Step 2:

[1234] Input: Data containing user information is sent to the server.

[1235] How it works: The server analyzes the received user information and stores the data in a database.

[1236] Output: User information is saved in the database of the cloud server.

[1237] Step 3:

[1238] Input: A dataset is created based on the stored user information to be input into the generative artificial intelligence model.

[1239] How it works: The server uses a generative artificial intelligence model to collect information about health insurance and payment services from the internet. It obtains information using web scraping technology and APIs.

[1240] Output: The collected information on medical insurance services and payment services is sent to the server and organized.

[1241] Step 4:

[1242] Input: Collected and organized information on health insurance services and payment services is passed to the generative AI model.

[1243] How it works: The server analyzes the collected information using a generative artificial intelligence model and selects the most appropriate insurance product and payment method based on the user information.

[1244] Output: A list of optimal insurance products and payment methods is generated as a result of the analysis.

[1245] Step 5:

[1246] Input: A list of insurance products and payment methods is generated as a result of the analysis.

[1247] How it works: The server sends the generated list to the smartphone app.

[1248] Output: Detailed information on the best insurance products and payment methods for your smartphone app will be displayed.

[1249] Step 6:

[1250] Input: The user enters a follow-up question.

[1251] How it works: A user enters and submits a follow-up question through a smartphone app. The server uses a generative artificial intelligence model to generate an answer for the received question.

[1252] Output: The generated answers are displayed on the smartphone app.

[1253] Step 7:

[1254] Input: A question that the generative artificial intelligence model cannot answer is input.

[1255] How it works: The server lists appropriate contact points for users when they have questions that the generative AI model cannot answer, and provides them to them.

[1256] Output: A list of contacts is displayed on the smartphone app.

[1257] The above are the specific processing steps of this system's program. Through this series of processes, users can find the most suitable medical insurance product and payment method for them and obtain detailed information.

[1258] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1259] The system of the present invention is composed of a combination of software and hardware for implementing a series of processes to propose optimal medical insurance products based on user information. This system also includes an emotion engine that recognizes the user's emotions, enabling more personalized proposals and responses depending on the user's emotional state.

[1260] System Components

[1261] 1. User Interface (Terminal):

[1262] It displays a form for users to enter their information, including age, gender, medical history, current medical history, and insurance preferences.

[1263] Emotion data is acquired using a camera or microphone while the user is typing.

[1264] 2. Information receiving and storage means (server):

[1265] Receives user information and emotion data sent from the device and stores it in a database.

[1266] 3. Generative AI model (server):

[1267] A generative artificial intelligence model (e.g., GPT model) is used to collect information on health insurance services from the Internet based on user information.

[1268] Analyze insurance service information and select the insurance product that best suits the user's needs.

[1269] 4. Emotion engine (server):

[1270] The system recognizes emotions from user input and interactions, and reflects the results in insurance product suggestions and responses.

[1271] 5. Information provision means (terminal):

[1272] Provide users with detailed information on the most suitable insurance products.

[1273] Generate answers to follow-up questions from the user.

[1274] Present information and respond in different formats based on the user's emotional state.

[1275] 6. Contact information providing means (server):

[1276] For questions that the generative artificial intelligence model cannot answer, a list of appropriate contact points is provided to the user.

[1277] Explanation of program processing

[1278] Entering and saving user information

[1279] The user inputs their age, gender, medical history, current medical history, and insurance needs. The device receives this information and, upon pressing the send button, sends it to the server. During this process, the device's camera and microphone are used to capture the user's emotional data. The server checks the received information and emotional data and stores it in a database.

[1280] Health insurance service survey

[1281] The server uses a generative artificial intelligence model based on the stored user information to collect online health insurance service information, including through web scraping and API usage, and organizes and filters the collected data to best meet the user's needs.

[1282] Insurance product analysis and selection

[1283] The server analyzes the collected information on medical insurance services using a generative AI model and selects the most suitable insurance product based on the user's information and emotional data. This selected information is prepared as data to be presented to the user.

[1284] Utilizing Emotional Data

[1285] The emotion engine (server) analyzes the stored emotional data and determines whether the user is relaxed, stressed, etc. Based on this information, the generative AI model presents detailed information about insurance products to the user.

[1286] Presenting results and responding to follow-up questions

[1287] The terminal displays a list of selected optimal insurance products and their detailed information to the user. The user can then enter additional questions based on this information. The server receives the additional questions and requests a generative AI model to generate answers. Information is provided in an appropriate format based on the user's emotional state.

[1288] Dealing with unanswerable questions

[1289] For questions that the generative artificial intelligence model cannot answer, the server will provide the user with a list of appropriate contact points.

[1290] Specific examples

[1291] As a concrete example, let's say a 30-year-old female user has a medical history of asthma, no current medical history, and is seeking generous coverage for hospitalization expenses. The user enters this information into a terminal and sends it. The server receives the information and requests analysis from a generative AI model. The AI ​​collects publicly available medical insurance information and lists the following insurance products:

[1292] Insurance A: Basic medical insurance, covers 80% of hospital costs

[1293] Insurance B: Covers 100% of hospital costs, but is expensive for people with asthma

[1294] Insurance C: A plan that covers 90% of hospitalization costs and is specifically designed for asthma.

[1295] The generative AI model further analyzes this information and proposes the most suitable insurance C to the user. The emotion engine analyzes the user's emotions and presents the information in an appropriate format based on stress and sense of security. The proposal is presented to the user along with detailed benefits, providing peace of mind to the user.

[1296] The processing flow will be explained below.

[1297] Step 1:

[1298] The user accesses the terminal interface and inputs information such as age, sex, medical history, current medical history, and insurance requirements.

[1299] Step 2:

[1300] The device receives information input by the user and simultaneously acquires the user's emotional data using the device's camera and microphone.

[1301] Step 3:

[1302] When the terminal presses the send button, the user information and emotion data are sent to the server.

[1303] Step 4:

[1304] The server receives the information and emotion data sent by the user and stores them in a database.

[1305] Step 5:

[1306] Based on the stored user information, the server generates a request to the generative artificial intelligence model to investigate medical insurance services.

[1307] Step 6:

[1308] The server collects this information by performing web scraping and API requests to collect information about health insurance services on the Internet.

[1309] Step 7:

[1310] The server analyzes the medical insurance service information collected and compares it with user information to perform filtering.

[1311] Step 8:

[1312] The generative artificial intelligence model (server) uses the analytical data to select the insurance product that best suits the user's needs.

[1313] Step 9:

[1314] Based on the analysis results received by the server from the generative artificial intelligence model, the server generates detailed information and a proposal for the optimal insurance product.

[1315] Step 10:

[1316] The server uses an emotion engine to analyze the stored emotion data and determine the user's current emotional state.

[1317] Step 11:

[1318] Based on the analysis results from the emotion engine, the server optimizes detailed information and proposals for insurance products in a format that corresponds to the user's emotional state.

[1319] Step 12:

[1320] The terminal displays the optimized insurance product list and detailed information received from the server to the user.

[1321] Step 13:

[1322] The user checks the displayed insurance information, and if there are any additional questions, enters them in the input form and presses the submit button.

[1323] Step 14:

[1324] The server receives a follow-up question from the user and requests the generative artificial intelligence model to generate an answer.

[1325] Step 15:

[1326] The generative artificial intelligence model (server) generates optimal answers to follow-up questions from the user and sends them back to the server.

[1327] Step 16:

[1328] The server receives the response from the generative artificial intelligence model and converts it into a data format for display to the user.

[1329] Step 17:

[1330] The terminal displays the answer received from the server to the user.

[1331] Step 18:

[1332] For questions that the generative artificial intelligence model cannot answer, the server lists appropriate contact points and presents them to the user.

[1333] Example 2

[1334] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1335] Conventional medical insurance product recommendation systems have difficulty proposing optimal insurance products that meet the individual needs of users, and have a particular problem of lacking personalized recommendations based on emotional states. Another problem is that when the generative AI model cannot answer a question, it takes time and effort for the user to find the appropriate contact point.

[1336] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1337] In this invention, the server includes means for receiving and storing personal information such as the user's age, gender, medical history, current medical history, and insurance needs, means for automatically collecting information on medical insurance services from the Internet using a generative AI model, means for analyzing the collected information on medical insurance services based on the user's personal information and selecting the most suitable insurance product, means for acquiring the user's emotional data and customizing the insurance product proposal content and information provision format based on the data, means for presenting the user with details of the most suitable insurance product and generating answers to additional questions, and means for presenting an appropriate contact point if the generative AI model cannot provide an answer. This makes it possible to propose personalized insurance products based on emotions and quickly provide contact points for inquiries to the user.

[1338] "User's personal information" refers to information that is useful for identifying a user and proposing insurance products, such as the user's age, gender, medical history, current medical history, and insurance needs.

[1339] A "generative AI model" refers to a model that uses artificial intelligence technology to collect information from the internet and generate data based on that information.

[1340] "Means of automatically collecting information on health insurance services from the Internet" refers to the process of automatically collecting information on health insurance services that are publicly available on the Internet using technologies such as web scraping and the use of APIs.

[1341] "Means for acquiring emotional data" refers to technology that uses hardware such as cameras and microphones to collect the user's facial expressions and voice, and then analyzes the user's emotional state based on this.

[1342] "Means for selecting the most suitable insurance product" refers to technology that analyzes the user's personal information and collected information on medical insurance services to select the insurance product that is most suitable for the user.

[1343] "Means for customizing the content of suggestions and the format of information provided" refers to the process of adjusting and customizing the content and format of information provided to users based on the acquired emotional data.

[1344] "Means for providing appropriate contact information" refers to technology for providing relevant contact information to the user when the generative AI model is unable to answer the user's follow-up question.

[1345] The system of the present invention is comprised of a combination of software and hardware for implementing a series of processes to recommend optimal health insurance products based on user information. The system includes an emotion engine that recognizes the user's emotional state and makes personalized recommendations accordingly.

[1346] System Components

[1347] 1. User Interface (Terminal):

[1348] It displays a form for users to enter their information, including age, gender, medical history, current medical history, and insurance preferences.

[1349] Emotion data is acquired using a camera or microphone while the user is typing.

[1350] 2. Information receiving and storage means (server):

[1351] Receives user information and emotion data sent from the device and stores it in a database.

[1352] 3. Generative AI model (server):

[1353] A generative artificial intelligence model (e.g., GPT model) is used to collect information on health insurance services from the Internet based on user information.

[1354] Analyze insurance service information and select the insurance product that best suits the user's needs.

[1355] 4. Emotion engine (server):

[1356] The system recognizes emotions from user input and interactions, and reflects the results in insurance product suggestions and responses.

[1357] 5. Information provision means (terminal):

[1358] Provide users with detailed information on the most suitable insurance products.

[1359] Generate answers to follow-up questions from the user.

[1360] Present information and respond in different formats based on the user's emotional state.

[1361] 6. Contact information providing means (server):

[1362] For questions that the generative AI model cannot answer, a list of appropriate contact points is provided to the user.

[1363] Explanation of program processing

[1364] Entering and saving user information

[1365] The user inputs their age, gender, medical history, current medical history, and insurance needs on the device. The device receives this information, and when the user presses the send button, emotional data obtained using the camera and microphone is sent to the server along with the information.

[1366] Collection of medical insurance service information

[1367] The server uses stored user information to gather health insurance service information from the internet using a generative artificial intelligence model, including web scraping and API usage.

[1368] Insurance product analysis and selection

[1369] The server uses a generative AI model to analyze the collected information on medical insurance services. Based on user information and emotional data, it selects the most suitable insurance product and organizes the proposal.

[1370] Utilizing Emotional Data

[1371] The emotion engine (server) analyzes the stored emotion data and determines whether the user is relaxed or stressed. Based on this result, it customizes the format and content of the information provided.

[1372] Presenting optimal insurance products and responding to additional questions

[1373] The terminal displays a list of the best insurance products and their details to the user. The information provided to the user is presented in an appropriate format based on their emotional state. The user can then enter additional questions based on the information presented.

[1374] Responding to any additional questions and providing contact information

[1375] The server receives the user's follow-up questions and asks the generative AI model to generate an answer. If the generative AI model cannot answer the question, the server lists appropriate contact points and provides them to the user.

[1376] Specific examples

[1377] As a concrete example, let's say a 30-year-old female user has a medical history of asthma, no current medical history, and is seeking generous coverage for hospitalization expenses. The user enters this information into a terminal and sends it. The server receives the information and requests analysis from a generative AI model. The generative AI collects publicly available medical insurance information and lists the following insurance products:

[1378] Insurance A: Basic medical insurance, covers 80% of hospital costs

[1379] Insurance B: Covers 100% of hospital costs, but is expensive for people with asthma

[1380] Insurance C: A plan that covers 90% of hospitalization costs and is specifically designed for asthma.

[1381] The server further analyzes this information and proposes the most suitable insurance C to the user. The emotion engine analyzes the user's emotions and presents the information in an appropriate format based on stress and sense of security. The proposal is presented to the user along with detailed benefits, providing peace of mind to the user.

[1382] Example of input prompt for the generative AI model to be used

[1383] "I'm a 30-year-old woman with a history of asthma and no current medical conditions. I'm looking for medical insurance that provides comprehensive coverage for hospitalization costs. Which insurance would be best for me?"

[1384] This system automatically suggests the most suitable medical insurance product based on the user's input information and emotional data, and provides personalized responses based on emotions, making it easier for users to find the insurance product that is most suitable for them.

[1385] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1386] Program processing steps

[1387] Step 1: Enter user information and obtain emotion data

[1388] The user enters their age, gender, medical history, current medical history, and insurance needs into the input form on the terminal. The input information is received by the terminal.

[1389] Input: Personal information entered by the user (e.g., age, gender, medical history, current medical history, insurance requests)

[1390] Output: Received user information and emotion data

[1391] Specific operation: The user enters information into the form, such as "30 years old, female, with asthma, looking for insurance that covers hospitalization costs generously." The device then activates the camera to capture the user's facial expressions and uses the microphone to obtain voice data.

[1392] Step 2: Send and store user information and emotion data

[1393] The device sends the acquired user information and emotion data to the server, which then stores the received information in a database.

[1394] Input: Received user information and emotion data

[1395] Output: User information and emotion data stored in a database

[1396] Specific operation: The device sends information such as "Age: 30, Gender: Female, Medical history: Asthma, Current medical history: None, Request: Coverage of hospital expenses, Emotional data: Stress level 3, Peace of mind level 2" to the server, which then stores it in a database.

[1397] Step 3: Collect health insurance service information

[1398] The server uses a generative artificial intelligence model based on the stored user information to gather information about health insurance services from the internet, including through web scraping and API usage.

[1399] Input: User information stored in the database

[1400] Output: Collected health insurance service information

[1401] Specific operation: The server inputs the following prompt sentence into the generative AI model: "30 years old, female, with a history of asthma, looking for medical insurance that prioritizes hospitalization costs. Please give me a list of recommended insurance products." The generative AI model collects medical insurance information from the web and returns a list of multiple insurance products such as "Insurance A, Insurance B, Insurance C."

[1402] Step 4: Insurance product analysis and selection

[1403] The server analyzes the collected information on medical insurance services using a generative AI model, and selects the most suitable insurance product based on user information and emotional data.

[1404] Input: Collected medical insurance service information, user information, and emotion data

[1405] Output: Recommendations for optimal insurance products

[1406] Specific operation: The server selects Insurance C, which best suits the user's needs, from the collected information: Insurance A (80% coverage), Insurance B (100% coverage, high asthma premium), and Insurance C (90% coverage). The selected information is organized into a detailed proposal.

[1407] Step 5: Analyze emotional data and customize information

[1408] The emotion engine (server) analyzes the stored emotion data to determine the user's emotional state, and customizes the information presentation format based on this result.

[1409] Input: Emotion data

[1410] Output: customized information format

[1411] Specific operation: The emotion engine (server) determines from the emotion data that the user's stress level is high and generates a detailed and polite explanation.

[1412] Step 6: Present the best insurance product and receive additional questions

[1413] The terminal displays a list of the best insurance products and their details to the user, who can then enter additional questions based on the information displayed.

[1414] Input: Recommendation for the best insurance product

[1415] Output: Insurance product details presented to the user

[1416] What happens: The device displays detailed information such as "Insurance C: A plan that covers 90% of hospitalization costs. Specializes in asthma, monthly premium is 8000 yen." The user then enters an additional question: "What benefits does Insurance C offer?"

[1417] Step 7: Generate answers to follow-up questions

[1418] The server receives the user's follow-up question, asks the generative AI model to generate an answer, and presents the generated answer to the user.

[1419] Input: User's additional question

[1420] Output: The generated answer

[1421] Specific operation: The server inputs "Additional question from the user: What benefits does Insurance C offer?" into the generative AI model, obtains an answer from the generative AI model such as "Insurance C offers benefits such as transportation subsidies for hospitalization and online consultations with a dedicated doctor," and presents this to the user.

[1422] Step 8: Provide contact information for unanswered questions

[1423] For questions that the generative AI model cannot answer, the server lists appropriate contact points and provides them to the user.

[1424] Input: Notification that the generative AI model is unable to answer the question

[1425] Output: Appropriate contact information

[1426] Specific operation: The server investigates questions that the generative AI model cannot answer and provides the user with information such as "For more information, please contact the following: Insurance Company C, Inquiry Number: XXX-XXXX-XXXX."

[1427] (Application example 2)

[1428] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1429] Conventional medical insurance product recommendation systems make recommendations without taking the user's emotional state into consideration, which can make it difficult for users to understand and accept them with a sense of security. While conventional systems can collect information and make recommendations using generative artificial intelligence models, they lack personalization using emotional data. When a user is stressed or not relaxed, it is difficult to provide appropriate information, and there is a need to improve user satisfaction.

[1430] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1431] In this invention, the server includes means for receiving and storing information such as the user's age, gender, medical history, current medical history, and insurance needs, means including an emotion engine for recognizing the user's emotions, means for automatically collecting information on medical insurance services from the Internet using a generative artificial intelligence model, means for analyzing the collected information on medical insurance services based on the user's information and emotional data and selecting an optimal insurance product, means for presenting details of the optimal insurance product in a format appropriate to the user's emotional state and generating answers to follow-up questions, and means for presenting an appropriate contact point if the generative artificial intelligence model cannot provide an answer. This makes it possible to propose individual insurance products according to the user's emotional state and to propose information that the user can accept with peace of mind.

[1432] "User information" refers to personal information entered by the user, such as age, gender, medical history, current medical history, and insurance requests.

[1433] An "emotion engine" is a system or software for recognizing user emotions and analyzing that data.

[1434] A "generative artificial intelligence model" is a system that includes machine learning algorithms to generate appropriate information and make suggestions based on user input.

[1435] A "user interface" is a system or screen configuration that provides a screen or form for a user to enter information.

[1436] "Emotional data" refers to emotional data based on facial expressions, tone of voice, body movements, etc., captured using the user's camera or microphone.

[1437] "Web scraping" is a technical method for automatically collecting data from the Internet.

[1438] "Insurance service information" refers to information about the most suitable medical insurance products that meet the user's needs.

[1439] "Contact Information" refers to the appropriate contact point for the user to obtain additional information or assistance if the generative AI model is unable to provide an answer.

[1440] "Pre-selection" is the process of selecting the most suitable medical insurance product based on user information.

[1441] System Configuration

[1442] The system according to the present invention comprises the following elements:

[1443] 1. User Interface (Terminal):

[1444] It displays a form for the user to enter their information, including age, gender, medical history, current medical history, and insurance preferences.

[1445] Emotion data is acquired using a camera or microphone while the user is typing.

[1446] 2. Information receiving and storage means (server):

[1447] Receives user information and emotion data sent from the device and stores it in a database.

[1448] 3. Generative AI model (server):

[1449] A generative artificial intelligence model is used to collect information on medical insurance services from the Internet based on user information.

[1450] Analyze insurance service information and select the insurance product that best suits the user's needs.

[1451] 4. Emotion engine (server):

[1452] The system recognizes emotions from user input and interactions, and reflects the results in insurance product suggestions and responses.

[1453] 5. Information provision means (terminal):

[1454] Provide users with detailed information on the most suitable insurance products.

[1455] Generate answers to follow-up questions from the user.

[1456] Present information and respond in different formats based on the user's emotional state.

[1457] 6. Contact information providing means (server):

[1458] For questions that the generative artificial intelligence model cannot answer, a list of appropriate contact points is provided to the user.

[1459] Program processing

[1460] The server receives the user's age, gender, medical history, current medical history, and insurance needs through the device when the user enters them, as well as emotional data captured using the camera and microphone when the user enters the information, and stores this in a database.

[1461] The generative AI model automatically collects information about health insurance services from the internet based on the received user information. This information is collected using web scraping technology. The collected data is analyzed, and the optimal insurance product is selected taking into account the user's information and emotional data.

[1462] The emotion engine analyzes the stored emotional data to determine whether the user is relaxed, stressed, etc. Based on this emotional information, the generative AI model selects detailed information about insurance products and presents them to the user in an appropriate format.

[1463] The terminal displays a list of selected optimal insurance products and their detailed information to the user. The user can then enter additional questions based on this information. The server receives the additional questions and requests the generative AI model to generate an answer. Information is provided in an appropriate format based on the user's emotional state. For questions that the generative AI model cannot answer, the server lists appropriate contact points and provides them to the user.

[1464] Specific examples

[1465] As a concrete example, consider a 30-year-old female user with a medical history of asthma, no current medical history, and a desire for generous coverage for hospitalization expenses. The user enters this information into a terminal and sends it. The server receives the information and requests analysis from a generative AI model. The AI ​​collects publicly available medical insurance information and lists the following insurance products, for example:

[1466] Insurance A: Basic medical insurance, covers 80% of hospital costs

[1467] Insurance B: Covers 100% of hospital costs, but is expensive for people with asthma

[1468] Insurance C: A plan that covers 90% of hospitalization costs and is specifically designed for asthma.

[1469] The generative AI model further analyzes this information and proposes the most suitable insurance C to the user. The emotion engine analyzes the user's emotions, and the information is presented in an appropriate format based on stress and sense of security.

[1470] Example prompts to input to the generative AI model

[1471] User info:

[1472] Age: 30

[1473] Gender: Female

[1474] Medical history: Asthma

[1475] Current medical status: None

[1476] Insurance needs: Comprehensive hospitalization coverage

[1477] User emotion analysis:

[1478] Current emotion: Nervous

[1479] Based on the above information and current emotion, what is the best health insurance plan for this user? Provide detailed plan options.

[1480] This prompt is then fed into a generative AI model, which then suggests appropriate insurance products in real time and can flexibly respond to changes in the user's emotions.

[1481] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1482] Specific explanations divided into processing steps

[1483] Step 1:

[1484] Entering and saving user information

[1485] The user enters their age, gender, medical history, current medical history, and insurance needs. The device receives this information and, upon pressing the send button, sends it to the server. During this process, the device's camera and microphone are used to acquire the user's emotional data. The input is the user's personal information and emotional data, and the output is sent to the server. Data processing involves converting and saving the user information and emotional data in a format with metadata.

[1486] Step 2:

[1487] Store user information and emotional data

[1488] The server receives user information and emotion data sent from the device. The received information is stored in a database. User information and emotion data are sent to the server as input, and this information is stored in a database as output. Data processing involves normalizing and verifying the received information.

[1489] Step 3:

[1490] Gathering information on medical insurance services

[1491] The server uses a generative artificial intelligence model based on the stored user information to collect online health insurance service information. Web scraping technology is used for collection. User information and website URLs are input, and health insurance service data is obtained as output. Data processing involves structuring the collected data and filtering out information relevant to the user.

[1492] Step 4:

[1493] Analysis and selection of medical insurance service information

[1494] The server analyzes the collected medical insurance service information using a generative AI model and selects the most suitable insurance product based on user information and emotional data. The input is medical insurance service information and user information, and the output is a list of the most suitable insurance products. In data processing, the emotional data is linked to the user's needs, and the generative AI model selects the appropriate insurance product.

[1495] Step 5:

[1496] Emotion data analysis and results presentation

[1497] The emotion engine analyzes the stored emotion data and determines whether the user is relaxed or stressed. Based on this information, the server presents the user with detailed information on the most suitable insurance products. The input is emotion data and a list of the most suitable insurance products, and the output is a customized presentation format and information content. Specifically, if the user is feeling stressed, softer language and explanation methods are used.

[1498] Step 6:

[1499] Generate answers to additional questions

[1500] The user can enter additional questions based on the insurance product presented. The device receives these and sends them to the server. The server then requests the generative AI model to generate an answer. The input is the additional question and related user information, and the output is the generated answer, which is displayed on the device. In terms of data calculation, the generative AI model generates an appropriate answer from a number of information sources.

[1501] Step 7:

[1502] Providing contact information

[1503] For questions that the generative AI model cannot answer, the server lists appropriate contact points and provides them to the user. The input is the unanswerable question and user information, and the output is the contact information displayed to the user. Data processing involves searching the knowledge base of the generative AI model for the most appropriate contact point.

[1504] Specific example processing explanation

[1505] For example, when a 30-year-old female user seeks insurance advice, she enters information into the device and uses a camera to collect facial expression data. The server receives and stores this data, then uses a generative AI model to select the most suitable insurance product. An emotion engine then analyzes the user's emotions and presents information based on the stress the user is feeling. Finally, the generative AI model suggests insurance products and responds to any follow-up questions.

[1506] Example prompts to input to a generative AI model:

[1507] User info:

[1508] Age: 30

[1509] Gender: Female

[1510] Medical history: Asthma

[1511] Current medical status: None

[1512] Insurance needs: Comprehensive hospitalization coverage

[1513] User emotion analysis:

[1514] Current emotion: Nervous

[1515] Based on the above information and current emotion, what is the best health insurance plan for this user? Provide detailed plan options.

[1516] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1517] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1518] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1519] [Fourth embodiment]

[1520] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1521] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1522] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1523] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1524] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1525] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1526] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1527] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1528] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1529] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1530] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1531] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1532] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1533] The system of the present invention is composed of a combination of software and hardware for implementing a series of processes to propose optimal medical insurance products based on user information. This system provides an interface for inputting the user's age, gender, medical history, current medical history, insurance needs, etc., and then uses a generative artificial intelligence model to research and analyze medical insurance services and propose optimal insurance products to the user.

[1534] System Components

[1535] 1. User Interface (Terminal):

[1536] It displays a form for users to enter their information, including age, gender, medical history, current medical history, and insurance preferences.

[1537] 2. Information receiving and storage means (server):

[1538] Receives user information sent from the device and stores it in a database.

[1539] 3. Generative AI model (server):

[1540] A generative artificial intelligence model (e.g., GPT model) is used to collect information on health insurance services from the Internet based on user information.

[1541] Analyze insurance service information and select the insurance product that best suits the user's needs.

[1542] 4. Information provision means (terminal):

[1543] Provide users with detailed information on the most suitable insurance products.

[1544] Generate answers to follow-up questions from the user.

[1545] 5. Contact information providing means (server):

[1546] For questions that the generative artificial intelligence model cannot answer, a list of appropriate contact points is provided to the user.

[1547] Explanation of program processing

[1548] Entering and saving user information

[1549] The user enters their age, gender, medical history, current medical history, and insurance needs. The device receives this information and, when they press the send button, it is sent to the server. The server verifies the received information and saves it in a database.

[1550] Health insurance service survey

[1551] The server uses a generative artificial intelligence model based on the stored user information to collect online health insurance service information, including through web scraping and API usage, and organizes and filters the collected data to best meet the user's needs.

[1552] Insurance product analysis and selection

[1553] The server analyzes the collected information on medical insurance services using a generative AI model and selects the most suitable insurance product based on the user information. This selected information is prepared as data to be presented to the user.

[1554] Presenting results and responding to follow-up questions

[1555] The terminal displays a list of the best insurance products and their details to the user. The user can then enter additional questions based on the list. The server receives the additional questions and generates answers using a generative AI model.

[1556] Dealing with unanswerable questions

[1557] For questions that the generative artificial intelligence model cannot answer, the server will provide the user with a list of appropriate contact points.

[1558] Specific examples

[1559] As a concrete example, let's say a 30-year-old female user has a medical history of asthma, no current medical history, and is seeking generous coverage for hospitalization expenses. The user enters this information into a terminal and sends it. The server receives the information and requests analysis from a generative AI model. The AI ​​collects publicly available medical insurance information and lists the following insurance products:

[1560] Insurance A: Basic medical insurance, covers 80% of hospital costs

[1561] Insurance B: Covers 100% of hospital costs, but is expensive for people with asthma

[1562] Insurance C: A plan that covers 90% of hospitalization costs and is specifically designed for asthma.

[1563] The generative AI model further analyzes this information and proposes the most suitable insurance C to the user. The proposal is presented to the user along with detailed benefits, providing peace of mind to the user.

[1564] The processing flow will be explained below.

[1565] Step 1:

[1566] The user accesses the terminal interface and inputs information such as age, sex, medical history, current medical history, and insurance requirements.

[1567] Step 2:

[1568] The terminal receives information entered by the user, and when the send button is pressed, the information is sent to the server.

[1569] Step 3:

[1570] The server receives the user information sent from the terminal, verifies the information, and then stores it in a database.

[1571] Step 4:

[1572] Based on the stored user information, the server generates a request to the generative artificial intelligence model to investigate medical insurance services.

[1573] Step 5:

[1574] The server collects this information by performing web scraping and API requests to collect information about health insurance services on the Internet.

[1575] Step 6:

[1576] The server analyzes the medical insurance service information collected and compares it with user information to perform filtering.

[1577] Step 7:

[1578] The generative artificial intelligence model (server) uses the analytical data to select the insurance product that best suits the user's needs.

[1579] Step 8:

[1580] Based on the analysis results received by the server from the generative artificial intelligence model, the server generates detailed information and a proposal for the optimal insurance product.

[1581] Step 9:

[1582] The terminal displays the optimum insurance product list and detailed information received from the server to the user.

[1583] Step 10:

[1584] The user checks the displayed insurance information, and if there are any additional questions, enters them in the input form and presses the submit button.

[1585] Step 11:

[1586] The server receives a follow-up question from the user and requests the generative artificial intelligence model to generate an answer.

[1587] Step 12:

[1588] The generative artificial intelligence model (server) generates optimal answers to follow-up questions from the user and sends them back to the server.

[1589] Step 13:

[1590] The server receives the response from the generative artificial intelligence model and converts it into a data format for display to the user.

[1591] Step 14:

[1592] The terminal displays the answer received from the server to the user.

[1593] Step 15:

[1594] For questions that the generative artificial intelligence model cannot answer, the server lists appropriate contact points and presents them to the user.

[1595] Example 1

[1596] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1597] Selecting medical insurance is extremely complex, and it takes time and money for users to find the best insurance product for them. It is also difficult to accurately select an insurance product that meets the needs of each individual user. Furthermore, there is a need for a system that can respond quickly and accurately to additional questions and detailed inquiries. A new system is needed to solve these problems.

[1598] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1599] In this invention, the server includes a means for receiving and storing data on a user's attributes, medical history, and insurance needs; a means for automatically collecting medical insurance service data from a network environment using a generative artificial intelligence model; and a means for analyzing the collected medical insurance service data based on the user's information and selecting an appropriate insurance product. This allows users to easily find the medical insurance product that best suits them. Furthermore, user convenience can be enhanced by providing prompt answers to follow-up questions and appropriate contact information when the generative artificial intelligence model cannot respond.

[1600] "User demographic information" means data regarding a user's age, gender, medical history, current medical history, and specific insurance needs and requirements.

[1601] "Medical history" refers to records of illnesses and treatments a user has experienced in the past.

[1602] A "generative artificial intelligence model" is a program that uses artificial intelligence technology to process natural language, gather information, and analyze data.

[1603] A "network environment" is an information technology infrastructure over which data communication takes place, such as the Internet or an internal network.

[1604] "Health insurance services" are insurance products and plans offered by insurance companies to cover medical expenses and treatment costs.

[1605] "Collection" is the act of gathering data or information for a specific purpose.

[1606] "Analysis" is the process of finding specific patterns and trends based on collected data and drawing evaluations and conclusions.

[1607] "Selection" is the act of choosing the most appropriate option from multiple options.

[1608] "Means for receiving and storing attribute information, medical history, and insurance request data" refers to technology that allows a server to receive and securely store information entered by a user through a terminal.

[1609] "Means for automatically collecting data on health insurance services from a network environment" refers to technology that uses web scraping and APIs to automatically collect necessary data from the Internet and other networks.

[1610] The "means for selecting appropriate insurance products" refers to a technology that enables a generative artificial intelligence model to find the most suitable insurance product based on the user's attribute information and medical history.

[1611] MODE FOR CARRYING OUT THE INVENTION

[1612] The present invention relates to a system that proposes optimal medical insurance products based on data such as user attribute information, medical history, insurance needs, etc. This system has the function of receiving user input information, collecting and analyzing information on medical insurance services using a generative artificial intelligence model, selecting the optimal insurance product, and presenting it to the user.

[1613] System Components

[1614] 1. User Interface (Terminal)

[1615] The terminal provides an interface for users to enter demographic information, medical history, and insurance requirements. This interface displays a form in which users can enter their age, gender, medical history, current medical history, and desired insurance conditions.

[1616] 2. Information receiving and storage means (server)

[1617] The server receives the information sent by the user, checks the content, and stores it in a database, making it possible to reliably manage individual user information.

[1618] 3. Data collection method (server)

[1619] The server uses a generative artificial intelligence model (e.g., GPT model) to automatically collect health insurance data from the network environment, using web scraping technology and APIs.

[1620] 4. Insurance product analysis and selection method (server)

[1621] The server analyzes the collected information on medical insurance services. The generative AI model selects the most suitable insurance product based on the information entered by the user. The analysis results are organized into a list of insurance products that meet the user's needs.

[1622] 5. Information provision means (terminal)

[1623] The terminal displays details of the insurance product provided by the server to the user, and if the user enters any additional questions, the terminal sends them to the server.

[1624] 6. Server for responding to additional questions

[1625] The server receives follow-up questions from the user and uses a generative artificial intelligence model to generate appropriate answers, which are then provided to the user in real time.

[1626] 7. Contact Information Providing Means (Server)

[1627] For questions that the generative artificial intelligence model cannot answer, the server lists appropriate contact information and provides it to the user.

[1628] Specific examples

[1629] For example, let's say a 30-year-old female user has a history of asthma, no current medical history, and wants medical insurance that provides generous coverage for hospitalization expenses. The user enters this information into the device and presses the send button. The server receives the information and uses a generative artificial intelligence model to collect publicly available medical insurance information. The collected data is organized in the following format:

[1630] Insurance A: Basic medical insurance, covers 80% of hospital costs

[1631] Insurance B: Covers 100% of hospital costs, but is expensive for people with asthma

[1632] Insurance C: A plan that covers 90% of hospitalization costs and is specifically designed for asthma.

[1633] The generative AI model analyzes this information and proposes the most suitable insurance C. The proposal, along with details, is presented to the user via their device. The user can then enter more detailed questions, and the server will use the generative AI model to instantly generate an answer. If the server cannot provide an answer, it will provide the appropriate contact information. This process allows users to easily find the medical insurance product that best suits them.

[1634] Prompt Sentence Examples

[1635] "I'm a 30-year-old woman with a medical history of asthma and no current medical conditions. I'm looking for coverage for hospitalization expenses. I'd like you to recommend the most suitable medical insurance product for me."

[1636] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1637] Step 1:

[1638] Users enter their age, gender, medical history, current medical history, and insurance needs into the terminal interface.

[1639] The information entered specifically includes data such as "Age: 30," "Gender: Female," "Medical history: Asthma," and "Insurance preference: Generous coverage for hospitalization expenses."

[1640] The terminal receives this information and temporarily stores it.

[1641] Step 2:

[1642] When the user presses the send button, the terminal sends the entered information to the server.

[1643] The server checks the received data and verifies that there are no omissions or errors.

[1644] The input is user information sent from the terminal.

[1645] The output is the verified user information saved in the database.

[1646] Step 3:

[1647] Based on the stored user information, the server uses a generative artificial intelligence model to collect information on medical insurance services from the network environment.

[1648] The input is user information and a collection prompt (e.g., "30 years old, female. Medical history of asthma, no current medical history. Prioritizing coverage for hospitalization expenses. Please suggest the most suitable medical insurance product.").

[1649] The output is collected health insurance service information data.

[1650] The data is collected using web scraping technology and APIs, specifically by retrieving the latest insurance plan information from insurance companies' official websites and public APIs.

[1651] Step 4:

[1652] The server analyzes the collected medical insurance service information using a generative artificial intelligence model.

[1653] The input is the insurance service data and user information collected in step 3.

[1654] The output is a list of insurance products that best suit the user's needs.

[1655] The generative artificial intelligence model used for the analysis is designed to select the most suitable insurance product based on the user's attribute information.

[1656] Step 5:

[1657] The server transmits the selected optimal insurance product list to the terminal.

[1658] The input is a list of the best insurance products.

[1659] The output is the insurance product details that are presented to the user.

[1660] The terminal displays the received information to the user.

[1661] As a specific example, details of "Insurance C: A plan that covers 90% of hospitalization costs and specializes in asthma" are displayed.

[1662] Step 6:

[1663] The user enters further detailed questions about the displayed insurance product into the terminal.

[1664] The terminal receives the input question and transmits it to the server.

[1665] The input is a follow-up question from the user (e.g., "Does Insurance C cover emergency transport costs?").

[1666] The output is query data to the server.

[1667] Step 7:

[1668] The server passes the received follow-up questions to a generative artificial intelligence model to generate appropriate answers.

[1669] The input is additional question data from the user.

[1670] The output is the generated answer.

[1671] The created answer is sent back to the terminal, which displays it to the user.

[1672] Step 8:

[1673] For questions that the generative artificial intelligence model cannot answer, the server lists appropriate contact points.

[1674] The input is question data that the generative artificial intelligence model cannot answer.

[1675] The output is a list of appropriate contacts.

[1676] This contact information is then sent back to the terminal, which provides it to the user.

[1677] (Application example 1)

[1678] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1679] Conventional medical insurance product proposal systems are limited to providing information on medical insurance products and are unable to fully meet the diverse needs of users. Furthermore, they do not propose optimal payment methods, including payment services, making it difficult for users to find the service that best suits them.

[1680] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1681] In this invention, the server includes means for receiving and storing information such as the user's age, gender, medical history, current medical history, payment history, purchase history, and insurance needs, means for automatically collecting information on medical insurance services and payment services from the Internet using a generative AI model, means for analyzing the collected information on medical insurance services and payment services based on the user's information and selecting the most appropriate insurance product and payment method, means for presenting the user with details of the most appropriate insurance product and payment method and generating answers to any additional questions, and means for presenting an appropriate contact point if the generative AI model cannot provide an answer. This makes it possible to propose optimal medical insurance products and payment methods that meet the diverse needs of users.

[1682] "User Information" refers to information such as the user's age, gender, medical history, current medical history, payment history, purchase history, and insurance requirements.

[1683] A "generative artificial intelligence model" refers to an artificial intelligence model that collects information from the Internet and makes optimal suggestions based on user information.

[1684] "Medical insurance services" refers to services that provide optimal medical insurance products based on the user's health condition and insurance needs.

[1685] "Payment services" refers to a means of providing optimal payment methods and discount services based on a user's payment history and purchase history.

[1686] "User interface" refers to the screens and forms through which users enter information and view system results.

[1687] "Web scraping" refers to the technique of automatically collecting information on the Internet.

[1688] "Contact Information" refers to the appropriate contact point for users to inquire about questions that the generative AI model cannot answer.

[1689] MODE FOR CARRYING OUT THE INVENTION

[1690] System Components

[1691] 1. User interface (smartphone app)

[1692] The user interface provides a form for users to enter their own information, including age, gender, medical history, current medical history, payment history, purchase history, and insurance needs. For example, by launching a smartphone app and entering information into the input form displayed on the screen, this data is sent to the system.

[1693] 2. Information receiving and storage means (cloud server)

[1694] The cloud server receives user information sent from the smartphone app and stores it in a database, where it is safely stored and can be accessed whenever necessary.

[1695] 3. Generative AI model (cloud server)

[1696] The cloud server uses a generative artificial intelligence model (e.g., GPT model) based on the stored user information to collect information on health insurance services and payment services from the internet. This includes using web scraping technology and APIs to quickly obtain the latest information. The collected data is organized on the cloud server and filtered in an optimal manner based on the user's needs.

[1697] 4. Insurance product analysis and selection

[1698] The cloud server analyzes the collected information on medical insurance services and payment services using a generative AI model. Based on the user information, it selects the most suitable insurance product and payment method, and prepares this selected information as data to present to the user.

[1699] 5. Information provision method (smartphone app)

[1700] The smartphone app displays detailed information on the optimal insurance product and payment method selected for the user. The user can then enter additional questions based on this information. The cloud server receives the additional questions and generates answers using a generative artificial intelligence model.

[1701] 6. Dealing with unanswerable questions

[1702] For questions that the generative AI model cannot answer, the cloud server will provide a list of appropriate contact points to the user, allowing them to quickly obtain the information they need.

[1703] Hardware and software used

[1704] The system is implemented using the following hardware and software:

[1705] Smartphone app (e.g. Android or iOS)

[1706] Cloud servers (e.g. AWS, Google Cloud, Microsoft Azure)

[1707] Databases (e.g. AWS RDS, Google Cloud SQL, Microsoft Azure SQL Database)

[1708] Generative AI models (e.g., OpenAI GPT-3, other AI models)

[1709] Web scraping technology (e.g., Beautiful Soup, Scrapy)

[1710] Specific examples

[1711] As a concrete example, consider a 30-year-old female user who has a medical history of asthma, no current medical history, spends a lot of money on groceries, and seeks generous coverage for hospital expenses. The user enters this information into an input form on a smartphone app and submits it. The cloud server receives the information and requests analysis from a generative artificial intelligence model. The AI ​​collects publicly available information on medical insurance and payment services, and lists the following insurance products and payment methods:

[1712] Insurance A: Basic medical insurance, covers 80% of hospital costs

[1713] Insurance B: Covers 100% of hospital costs, but is expensive for people with asthma

[1714] Insurance C: A plan that covers 90% of hospitalization costs and is specifically designed for asthma.

[1715] Payment Service A: Offers 5% cash back on grocery purchases

[1716] Payment Service B: Receive a 20% discount on grocery purchases over a certain amount each month

[1717] The generative AI model analyzes this information and recommends the most suitable insurance C and payment service A for the user. The recommendations, along with detailed benefits, are displayed on the smartphone app, providing peace of mind and savings opportunities for the user.

[1718] Prompt Sentence Examples

[1719] User Information:

[1720] Age: 30

[1721] Gender: Female

[1722] Payment history: Breakdown of monthly expenses (groceries 60%, eating out 20%, other 20%)

[1723] Insurance demand: cash back and discounts on groceries

[1724] Please suggest the best insurance product and payment service for this user."

[1725] The above is a specific embodiment for carrying out the invention, which allows users to receive proposals for optimal insurance products and payment methods that meet their diverse needs.

[1726] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1727] Step 1:

[1728] Input: The user enters information such as age, gender, medical history, current medical history, payment history, purchase history, and insurance requirements.

[1729] Action: A user uses a smartphone app to enter the required information into a form and presses the submit button.

[1730] Output: The entered user information is sent from the app to the server.

[1731] Step 2:

[1732] Input: Data containing user information is sent to the server.

[1733] How it works: The server analyzes the received user information and stores the data in a database.

[1734] Output: User information is saved in the database of the cloud server.

[1735] Step 3:

[1736] Input: A dataset is created based on the stored user information to be input into the generative artificial intelligence model.

[1737] How it works: The server uses a generative artificial intelligence model to collect information about health insurance and payment services from the internet. It obtains information using web scraping technology and APIs.

[1738] Output: The collected information on medical insurance services and payment services is sent to the server and organized.

[1739] Step 4:

[1740] Input: Collected and organized information on health insurance services and payment services is passed to the generative AI model.

[1741] How it works: The server analyzes the collected information using a generative artificial intelligence model and selects the most appropriate insurance product and payment method based on the user information.

[1742] Output: A list of optimal insurance products and payment methods is generated as a result of the analysis.

[1743] Step 5:

[1744] Input: A list of insurance products and payment methods is generated as a result of the analysis.

[1745] How it works: The server sends the generated list to the smartphone app.

[1746] Output: Detailed information on the best insurance products and payment methods for your smartphone app will be displayed.

[1747] Step 6:

[1748] Input: The user enters a follow-up question.

[1749] How it works: A user enters and submits a follow-up question through a smartphone app. The server uses a generative artificial intelligence model to generate an answer for the received question.

[1750] Output: The generated answers are displayed on the smartphone app.

[1751] Step 7:

[1752] Input: A question that the generative artificial intelligence model cannot answer is input.

[1753] How it works: The server lists appropriate contact points for users when they have questions that the generative AI model cannot answer, and provides them to them.

[1754] Output: A list of contacts is displayed on the smartphone app.

[1755] The above are the specific processing steps of this system's program. Through this series of processes, users can find the most suitable medical insurance product and payment method for them and obtain detailed information.

[1756] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1757] The system of the present invention is composed of a combination of software and hardware for implementing a series of processes to propose optimal medical insurance products based on user information. This system also includes an emotion engine that recognizes the user's emotions, enabling more personalized proposals and responses depending on the user's emotional state.

[1758] System Components

[1759] 1. User Interface (Terminal):

[1760] It displays a form for users to enter their information, including age, gender, medical history, current medical history, and insurance preferences.

[1761] Emotion data is acquired using a camera or microphone while the user is typing.

[1762] 2. Information receiving and storage means (server):

[1763] Receives user information and emotion data sent from the device and stores it in a database.

[1764] 3. Generative AI model (server):

[1765] A generative artificial intelligence model (e.g., GPT model) is used to collect information on health insurance services from the Internet based on user information.

[1766] Analyze insurance service information and select the insurance product that best suits the user's needs.

[1767] 4. Emotion engine (server):

[1768] The system recognizes emotions from user input and interactions, and reflects the results in insurance product suggestions and responses.

[1769] 5. Information provision means (terminal):

[1770] Provide users with detailed information on the most suitable insurance products.

[1771] Generate answers to follow-up questions from the user.

[1772] Present information and respond in different formats based on the user's emotional state.

[1773] 6. Contact information providing means (server):

[1774] For questions that the generative artificial intelligence model cannot answer, a list of appropriate contact points is provided to the user.

[1775] Explanation of program processing

[1776] Entering and saving user information

[1777] The user inputs their age, gender, medical history, current medical history, and insurance needs. The device receives this information and, upon pressing the send button, sends it to the server. During this process, the device's camera and microphone are used to capture the user's emotional data. The server checks the received information and emotional data and stores it in a database.

[1778] Health insurance service survey

[1779] The server uses a generative artificial intelligence model based on the stored user information to collect online health insurance service information, including through web scraping and API usage, and organizes and filters the collected data to best meet the user's needs.

[1780] Insurance product analysis and selection

[1781] The server analyzes the collected information on medical insurance services using a generative AI model and selects the most suitable insurance product based on the user's information and emotional data. This selected information is prepared as data to be presented to the user.

[1782] Utilizing Emotional Data

[1783] The emotion engine (server) analyzes the stored emotional data and determines whether the user is relaxed, stressed, etc. Based on this information, the generative AI model presents detailed information about insurance products to the user.

[1784] Presenting results and responding to follow-up questions

[1785] The terminal displays a list of selected optimal insurance products and their detailed information to the user. The user can then enter additional questions based on this information. The server receives the additional questions and requests a generative AI model to generate answers. Information is provided in an appropriate format based on the user's emotional state.

[1786] Dealing with unanswerable questions

[1787] For questions that the generative artificial intelligence model cannot answer, the server will provide the user with a list of appropriate contact points.

[1788] Specific examples

[1789] As a concrete example, let's say a 30-year-old female user has a medical history of asthma, no current medical history, and is seeking generous coverage for hospitalization expenses. The user enters this information into a terminal and sends it. The server receives the information and requests analysis from a generative AI model. The AI ​​collects publicly available medical insurance information and lists the following insurance products:

[1790] Insurance A: Basic medical insurance, covers 80% of hospital costs

[1791] Insurance B: Covers 100% of hospital costs, but is expensive for people with asthma

[1792] Insurance C: A plan that covers 90% of hospitalization costs and is specifically designed for asthma.

[1793] The generative AI model further analyzes this information and proposes the most suitable insurance C to the user. The emotion engine analyzes the user's emotions and presents the information in an appropriate format based on stress and sense of security. The proposal is presented to the user along with detailed benefits, providing peace of mind to the user.

[1794] The processing flow will be explained below.

[1795] Step 1:

[1796] The user accesses the terminal interface and inputs information such as age, sex, medical history, current medical history, and insurance requirements.

[1797] Step 2:

[1798] The device receives information input by the user and simultaneously acquires the user's emotional data using the device's camera and microphone.

[1799] Step 3:

[1800] When the terminal presses the send button, the user information and emotion data are sent to the server.

[1801] Step 4:

[1802] The server receives the information and emotion data sent by the user and stores them in a database.

[1803] Step 5:

[1804] Based on the stored user information, the server generates a request to the generative artificial intelligence model to investigate medical insurance services.

[1805] Step 6:

[1806] The server collects this information by performing web scraping and API requests to collect information about health insurance services on the Internet.

[1807] Step 7:

[1808] The server analyzes the medical insurance service information collected and compares it with user information to perform filtering.

[1809] Step 8:

[1810] The generative artificial intelligence model (server) uses the analytical data to select the insurance product that best suits the user's needs.

[1811] Step 9:

[1812] Based on the analysis results received by the server from the generative artificial intelligence model, the server generates detailed information and a proposal for the optimal insurance product.

[1813] Step 10:

[1814] The server uses an emotion engine to analyze the stored emotion data and determine the user's current emotional state.

[1815] Step 11:

[1816] Based on the analysis results from the emotion engine, the server optimizes detailed information and proposals for insurance products in a format that corresponds to the user's emotional state.

[1817] Step 12:

[1818] The terminal displays the optimized insurance product list and detailed information received from the server to the user.

[1819] Step 13:

[1820] The user checks the displayed insurance information, and if there are any additional questions, enters them in the input form and presses the submit button.

[1821] Step 14:

[1822] The server receives a follow-up question from the user and requests the generative artificial intelligence model to generate an answer.

[1823] Step 15:

[1824] The generative artificial intelligence model (server) generates optimal answers to follow-up questions from the user and sends them back to the server.

[1825] Step 16:

[1826] The server receives the response from the generative artificial intelligence model and converts it into a data format for display to the user.

[1827] Step 17:

[1828] The terminal displays the answer received from the server to the user.

[1829] Step 18:

[1830] For questions that the generative artificial intelligence model cannot answer, the server lists appropriate contact points and presents them to the user.

[1831] Example 2

[1832] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1833] Conventional medical insurance product recommendation systems have difficulty proposing optimal insurance products that meet the individual needs of users, and have a particular problem of lacking personalized recommendations based on emotional states. Another problem is that when the generative AI model cannot answer a question, it takes time and effort for the user to find the appropriate contact point.

[1834] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1835] In this invention, the server includes means for receiving and storing personal information such as the user's age, gender, medical history, current medical history, and insurance needs, means for automatically collecting information on medical insurance services from the Internet using a generative AI model, means for analyzing the collected information on medical insurance services based on the user's personal information and selecting the most suitable insurance product, means for acquiring the user's emotional data and customizing the insurance product proposal content and information provision format based on the data, means for presenting the user with details of the most suitable insurance product and generating answers to additional questions, and means for presenting an appropriate contact point if the generative AI model cannot provide an answer. This makes it possible to propose personalized insurance products based on emotions and quickly provide contact points for inquiries to the user.

[1836] "User's personal information" refers to information that is useful for identifying a user and proposing insurance products, such as the user's age, gender, medical history, current medical history, and insurance needs.

[1837] A "generative AI model" refers to a model that uses artificial intelligence technology to collect information from the internet and generate data based on that information.

[1838] "Means of automatically collecting information on health insurance services from the Internet" refers to the process of automatically collecting information on health insurance services that are publicly available on the Internet using technologies such as web scraping and the use of APIs.

[1839] "Means for acquiring emotional data" refers to technology that uses hardware such as cameras and microphones to collect the user's facial expressions and voice, and then analyzes the user's emotional state based on this.

[1840] "Means for selecting the most suitable insurance product" refers to technology that analyzes the user's personal information and collected information on medical insurance services to select the insurance product that is most suitable for the user.

[1841] "Means for customizing the content of suggestions and the format of information provided" refers to the process of adjusting and customizing the content and format of information provided to users based on the acquired emotional data.

[1842] "Means for providing appropriate contact information" refers to technology for providing relevant contact information to the user when the generative AI model is unable to answer the user's follow-up question.

[1843] The system of the present invention is comprised of a combination of software and hardware for implementing a series of processes to recommend optimal health insurance products based on user information. The system includes an emotion engine that recognizes the user's emotional state and makes personalized recommendations accordingly.

[1844] System Components

[1845] 1. User Interface (Terminal):

[1846] It displays a form for users to enter their information, including age, gender, medical history, current medical history, and insurance preferences.

[1847] Emotion data is acquired using a camera or microphone while the user is typing.

[1848] 2. Information receiving and storage means (server):

[1849] Receives user information and emotion data sent from the device and stores it in a database.

[1850] 3. Generative AI model (server):

[1851] A generative artificial intelligence model (e.g., GPT model) is used to collect information on health insurance services from the Internet based on user information.

[1852] Analyze insurance service information and select the insurance product that best suits the user's needs.

[1853] 4. Emotion engine (server):

[1854] The system recognizes emotions from user input and interactions, and reflects the results in insurance product suggestions and responses.

[1855] 5. Information provision means (terminal):

[1856] Provide users with detailed information on the most suitable insurance products.

[1857] Generate answers to follow-up questions from the user.

[1858] Present information and respond in different formats based on the user's emotional state.

[1859] 6. Contact information providing means (server):

[1860] For questions that the generative AI model cannot answer, a list of appropriate contact points is provided to the user.

[1861] Explanation of program processing

[1862] Entering and saving user information

[1863] The user inputs their age, gender, medical history, current medical history, and insurance needs on the device. The device receives this information, and when the user presses the send button, emotional data obtained using the camera and microphone is sent to the server along with the information.

[1864] Collection of medical insurance service information

[1865] The server uses stored user information to gather health insurance service information from the internet using a generative artificial intelligence model, including web scraping and API usage.

[1866] Insurance product analysis and selection

[1867] The server uses a generative AI model to analyze the collected information on medical insurance services. Based on user information and emotional data, it selects the most suitable insurance product and organizes the proposal.

[1868] Utilizing Emotional Data

[1869] The emotion engine (server) analyzes the stored emotion data and determines whether the user is relaxed or stressed. Based on this result, it customizes the format and content of the information provided.

[1870] Presenting optimal insurance products and responding to additional questions

[1871] The terminal displays a list of the best insurance products and their details to the user. The information provided to the user is presented in an appropriate format based on their emotional state. The user can then enter additional questions based on the information presented.

[1872] Responding to any additional questions and providing contact information

[1873] The server receives the user's follow-up questions and asks the generative AI model to generate an answer. If the generative AI model cannot answer the question, the server lists appropriate contact points and provides them to the user.

[1874] Specific examples

[1875] As a concrete example, let's say a 30-year-old female user has a medical history of asthma, no current medical history, and is seeking generous coverage for hospitalization expenses. The user enters this information into a terminal and sends it. The server receives the information and requests analysis from a generative AI model. The generative AI collects publicly available medical insurance information and lists the following insurance products:

[1876] Insurance A: Basic medical insurance, covers 80% of hospital costs

[1877] Insurance B: Covers 100% of hospital costs, but is expensive for people with asthma

[1878] Insurance C: A plan that covers 90% of hospitalization costs and is specifically designed for asthma.

[1879] The server further analyzes this information and proposes the most suitable insurance C to the user. The emotion engine analyzes the user's emotions and presents the information in an appropriate format based on stress and sense of security. The proposal is presented to the user along with detailed benefits, providing peace of mind to the user.

[1880] Example of input prompt for the generative AI model to be used

[1881] "I'm a 30-year-old woman with a history of asthma and no current medical conditions. I'm looking for medical insurance that provides comprehensive coverage for hospitalization costs. Which insurance would be best for me?"

[1882] This system automatically suggests the most suitable medical insurance product based on the user's input information and emotional data, and provides personalized responses based on emotions, making it easier for users to find the insurance product that is most suitable for them.

[1883] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1884] Program processing steps

[1885] Step 1: Enter user information and obtain emotion data

[1886] The user enters their age, gender, medical history, current medical history, and insurance needs into the input form on the terminal. The input information is received by the terminal.

[1887] Input: Personal information entered by the user (e.g., age, gender, medical history, current medical history, insurance requests)

[1888] Output: Received user information and emotion data

[1889] Specific operation: The user enters information into the form, such as "30 years old, female, with asthma, looking for insurance that covers hospitalization costs generously." The device then activates the camera to capture the user's facial expressions and uses the microphone to obtain voice data.

[1890] Step 2: Send and store user information and emotion data

[1891] The device sends the acquired user information and emotion data to the server, which then stores the received information in a database.

[1892] Input: Received user information and emotion data

[1893] Output: User information and emotion data stored in a database

[1894] Specific operation: The device sends information such as "Age: 30, Gender: Female, Medical history: Asthma, Current medical history: None, Request: Coverage of hospital expenses, Emotional data: Stress level 3, Peace of mind level 2" to the server, which then stores it in a database.

[1895] Step 3: Collect health insurance service information

[1896] The server uses a generative artificial intelligence model based on the stored user information to gather information about health insurance services from the internet, including through web scraping and API usage.

[1897] Input: User information stored in the database

[1898] Output: Collected health insurance service information

[1899] Specific operation: The server inputs the following prompt sentence into the generative AI model: "30 years old, female, with a history of asthma, looking for medical insurance that prioritizes hospitalization costs. Please give me a list of recommended insurance products." The generative AI model collects medical insurance information from the web and returns a list of multiple insurance products such as "Insurance A, Insurance B, Insurance C."

[1900] Step 4: Insurance product analysis and selection

[1901] The server analyzes the collected information on medical insurance services using a generative AI model, and selects the most suitable insurance product based on user information and emotional data.

[1902] Input: Collected medical insurance service information, user information, and emotion data

[1903] Output: Recommendations for optimal insurance products

[1904] Specific operation: The server selects Insurance C, which best suits the user's needs, from the collected information: Insurance A (80% coverage), Insurance B (100% coverage, high asthma premium), and Insurance C (90% coverage). The selected information is organized into a detailed proposal.

[1905] Step 5: Analyze emotional data and customize information

[1906] The emotion engine (server) analyzes the stored emotion data to determine the user's emotional state, and customizes the information presentation format based on this result.

[1907] Input: Emotion data

[1908] Output: customized information format

[1909] Specific operation: The emotion engine (server) determines from the emotion data that the user's stress level is high and generates a detailed and polite explanation.

[1910] Step 6: Present the best insurance product and receive additional questions

[1911] The terminal displays a list of the best insurance products and their details to the user, who can then enter additional questions based on the information displayed.

[1912] Input: Recommendation for the best insurance product

[1913] Output: Insurance product details presented to the user

[1914] What happens: The device displays detailed information such as "Insurance C: A plan that covers 90% of hospitalization costs. Specializes in asthma, monthly premium is 8000 yen." The user then enters an additional question: "What benefits does Insurance C offer?"

[1915] Step 7: Generate answers to follow-up questions

[1916] The server receives the user's follow-up question, asks the generative AI model to generate an answer, and presents the generated answer to the user.

[1917] Input: User's additional question

[1918] Output: The generated answer

[1919] Specific operation: The server inputs "Additional question from the user: What benefits does Insurance C offer?" into the generative AI model, obtains an answer from the generative AI model such as "Insurance C offers benefits such as transportation subsidies for hospitalization and online consultations with a dedicated doctor," and presents this to the user.

[1920] Step 8: Provide contact information for unanswered questions

[1921] For questions that the generative AI model cannot answer, the server lists appropriate contact points and provides them to the user.

[1922] Input: Notification that the generative AI model is unable to answer the question

[1923] Output: Appropriate contact information

[1924] Specific operation: The server investigates questions that the generative AI model cannot answer and provides the user with information such as "For more information, please contact the following: Insurance Company C, Inquiry Number: XXX-XXXX-XXXX."

[1925] (Application example 2)

[1926] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1927] Conventional medical insurance product recommendation systems make recommendations without taking the user's emotional state into consideration, which can make it difficult for users to understand and accept them with a sense of security. While conventional systems can collect information and make recommendations using generative artificial intelligence models, they lack personalization using emotional data. When a user is stressed or not relaxed, it is difficult to provide appropriate information, and there is a need to improve user satisfaction.

[1928] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1929] In this invention, the server includes means for receiving and storing information such as the user's age, gender, medical history, current medical history, and insurance needs, means including an emotion engine for recognizing the user's emotions, means for automatically collecting information on medical insurance services from the Internet using a generative artificial intelligence model, means for analyzing the collected information on medical insurance services based on the user's information and emotional data and selecting an optimal insurance product, means for presenting details of the optimal insurance product in a format appropriate to the user's emotional state and generating answers to follow-up questions, and means for presenting an appropriate contact point if the generative artificial intelligence model cannot provide an answer. This makes it possible to propose individual insurance products according to the user's emotional state and to propose information that the user can accept with peace of mind.

[1930] "User information" refers to personal information entered by the user, such as age, gender, medical history, current medical history, and insurance requests.

[1931] An "emotion engine" is a system or software for recognizing user emotions and analyzing that data.

[1932] A "generative artificial intelligence model" is a system that includes machine learning algorithms to generate appropriate information and make suggestions based on user input.

[1933] A "user interface" is a system or screen configuration that provides a screen or form for a user to enter information.

[1934] "Emotional data" refers to emotional data based on facial expressions, tone of voice, body movements, etc., captured using the user's camera or microphone.

[1935] "Web scraping" is a technical method for automatically collecting data from the Internet.

[1936] "Insurance service information" refers to information about the most suitable medical insurance products that meet the user's needs.

[1937] "Contact Information" refers to the appropriate contact point for the user to obtain additional information or assistance if the generative AI model is unable to provide an answer.

[1938] "Pre-selection" is the process of selecting the most suitable medical insurance product based on user information.

[1939] System Configuration

[1940] The system according to the present invention comprises the following elements:

[1941] 1. User Interface (Terminal):

[1942] It displays a form for the user to enter their information, including age, gender, medical history, current medical history, and insurance preferences.

[1943] Emotion data is acquired using a camera or microphone while the user is typing.

[1944] 2. Information receiving and storage means (server):

[1945] Receives user information and emotion data sent from the device and stores it in a database.

[1946] 3. Generative AI model (server):

[1947] A generative artificial intelligence model is used to collect information on medical insurance services from the Internet based on user information.

[1948] Analyze insurance service information and select the insurance product that best suits the user's needs.

[1949] 4. Emotion engine (server):

[1950] The system recognizes emotions from user input and interactions, and reflects the results in insurance product suggestions and responses.

[1951] 5. Information provision means (terminal):

[1952] Provide users with detailed information on the most suitable insurance products.

[1953] Generate answers to follow-up questions from the user.

[1954] Present information and respond in different formats based on the user's emotional state.

[1955] 6. Contact information providing means (server):

[1956] For questions that the generative artificial intelligence model cannot answer, a list of appropriate contact points is provided to the user.

[1957] Program processing

[1958] The server receives the user's age, gender, medical history, current medical history, and insurance needs through the device when the user enters them, as well as emotional data captured using the camera and microphone when the user enters the information, and stores this in a database.

[1959] The generative AI model automatically collects information about health insurance services from the internet based on the received user information. This information is collected using web scraping technology. The collected data is analyzed, and the optimal insurance product is selected taking into account the user's information and emotional data.

[1960] The emotion engine analyzes the stored emotional data to determine whether the user is relaxed, stressed, etc. Based on this emotional information, the generative AI model selects detailed information about insurance products and presents them to the user in an appropriate format.

[1961] The terminal displays a list of selected optimal insurance products and their detailed information to the user. The user can then enter additional questions based on this information. The server receives the additional questions and requests the generative AI model to generate an answer. Information is provided in an appropriate format based on the user's emotional state. For questions that the generative AI model cannot answer, the server lists appropriate contact points and provides them to the user.

[1962] Specific examples

[1963] As a concrete example, consider a 30-year-old female user with a medical history of asthma, no current medical history, and a desire for generous coverage for hospitalization expenses. The user enters this information into a terminal and sends it. The server receives the information and requests analysis from a generative AI model. The AI ​​collects publicly available medical insurance information and lists the following insurance products, for example:

[1964] Insurance A: Basic medical insurance, covers 80% of hospital costs

[1965] Insurance B: Covers 100% of hospital costs, but is expensive for people with asthma

[1966] Insurance C: A plan that covers 90% of hospitalization costs and is specifically designed for asthma.

[1967] The generative AI model further analyzes this information and proposes the most suitable insurance C to the user. The emotion engine analyzes the user's emotions, and the information is presented in an appropriate format based on stress and sense of security.

[1968] Example prompts to input to the generative AI model

[1969] User info:

[1970] Age: 30

[1971] Gender: Female

[1972] Medical history: Asthma

[1973] Current medical status: None

[1974] Insurance needs: Comprehensive hospitalization coverage

[1975] User emotion analysis:

[1976] Current emotion: Nervous

[1977] Based on the above information and current emotion, what is the best health insurance plan for this user? Provide detailed plan options.

[1978] This prompt is then fed into a generative AI model, which then suggests appropriate insurance products in real time and can flexibly respond to changes in the user's emotions.

[1979] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1980] Specific explanations divided into processing steps

[1981] Step 1:

[1982] Entering and saving user information

[1983] The user enters their age, gender, medical history, current medical history, and insurance needs. The device receives this information and, upon pressing the send button, sends it to the server. During this process, the device's camera and microphone are used to acquire the user's emotional data. The input is the user's personal information and emotional data, and the output is sent to the server. Data processing involves converting and saving the user information and emotional data in a format with metadata.

[1984] Step 2:

[1985] Store user information and emotional data

[1986] The server receives user information and emotion data sent from the device. The received information is stored in a database. User information and emotion data are sent to the server as input, and this information is stored in a database as output. Data processing involves normalizing and verifying the received information.

[1987] Step 3:

[1988] Gathering information on medical insurance services

[1989] The server uses a generative artificial intelligence model based on the stored user information to collect online health insurance service information. Web scraping technology is used for collection. User information and website URLs are input, and health insurance service data is obtained as output. Data processing involves structuring the collected data and filtering out information relevant to the user.

[1990] Step 4:

[1991] Analysis and selection of medical insurance service information

[1992] The server analyzes the collected medical insurance service information using a generative AI model and selects the most suitable insurance product based on user information and emotional data. The input is medical insurance service information and user information, and the output is a list of the most suitable insurance products. In data processing, the emotional data is linked to the user's needs, and the generative AI model selects the appropriate insurance product.

[1993] Step 5:

[1994] Emotion data analysis and results presentation

[1995] The emotion engine analyzes the stored emotion data and determines whether the user is relaxed or stressed. Based on this information, the server presents the user with detailed information on the most suitable insurance products. The input is emotion data and a list of the most suitable insurance products, and the output is a customized presentation format and information content. Specifically, if the user is feeling stressed, softer language and explanation methods are used.

[1996] Step 6:

[1997] Generate answers to additional questions

[1998] The user can enter additional questions based on the insurance product presented. The device receives these and sends them to the server. The server then requests the generative AI model to generate an answer. The input is the additional question and related user information, and the output is the generated answer, which is displayed on the device. In terms of data calculation, the generative AI model generates an appropriate answer from a number of information sources.

[1999] Step 7:

[2000] Providing contact information

[2001] For questions that the generative AI model cannot answer, the server lists appropriate contact points and provides them to the user. The input is the unanswerable question and user information, and the output is the contact information displayed to the user. Data processing involves searching the knowledge base of the generative AI model for the most appropriate contact point.

[2002] Specific example processing explanation

[2003] For example, when a 30-year-old female user seeks insurance advice, she enters information into the device and uses a camera to collect facial expression data. The server receives and stores this data, then uses a generative AI model to select the most suitable insurance product. An emotion engine then analyzes the user's emotions and presents information based on the stress the user is feeling. Finally, the generative AI model suggests insurance products and responds to any follow-up questions.

[2004] Example prompts to input to a generative AI model:

[2005] User info:

[2006] Age: 30

[2007] Gender: Female

[2008] Medical history: Asthma

[2009] Current medical status: None

[2010] Insurance needs: Comprehensive hospitalization coverage

[2011] User emotion analysis:

[2012] Current emotion: Nervous

[2013] Based on the above information and current emotion, what is the best health insurance plan for this user? Provide detailed plan options.

[2014] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2015] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2016] In the above embodimen...

Claims

1. A means of receiving and storing information such as the user's age, gender, medical history, current medical history, and insurance needs; A means for automatically collecting information on health insurance services from the Internet using a generative artificial intelligence model; A means for analyzing collected medical insurance service information based on user information and selecting the most suitable insurance product; a means for presenting details of the most suitable insurance product to the user and generating answers to any follow-up questions; A system that includes a means for presenting an appropriate point of contact when a generative artificial intelligence model cannot provide an answer.

2. 2. The system of claim 1, wherein the means for receiving and storing user information includes means for providing a user interface and means for displaying an input form.

3. The system of claim 1 , wherein the means for automatically collecting information about health insurance services from the Internet includes means using web scraping.

4. The system of claim 1, wherein the means for analyzing medical insurance service information and selecting the most appropriate insurance product based on user information includes a means for analyzing insurance products using a generative artificial intelligence model and outputting the most appropriate information based on the user's needs.

5. The system of claim 1 , wherein the means for presenting details of optimal insurance products to the user includes means for displaying a list of insurance products and detailed information through a user interface and means for providing an input field for additional questions.

6. 2. The system of claim 1, wherein the means for answering the follow-up questions includes means for generating answers to the questions from the user using a generative artificial intelligence model and converting the answers into a data format for display.

7. 2. The system according to claim 1, wherein the means for presenting a point of contact when the generative artificial intelligence model cannot provide an answer includes means for listing appropriate points of contact and displaying them to the user.

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